1-1 Programs 1-4 Review Quiz

Why and How Managers Plan Importance of planning The planing process Benefits of planning Planning and time management Types of PLans used by managers Long term and short term plans Strageic and tactical plans Operational plans Planning Tools and Techiqunes Forecasting Contrigency planning Scenario planning Benchmaking Use of staff planners Implementing Plans to Achive Results Goal setting Goal management Goal alignment Participation and involvement Planning Def: The process of setting objectives and determining how best to accomplish them Planning at Eaton Corporation “Making the hard decision before events force them upon you, an anticipating the future needs of the market before the demand asset itself Objectives and goals Identifity the specific results or desired outcomes that one intends to achieve Plan Def: A statement of action steps to be taken in order to accomplish the objectives (goals) Steps in the planning process: Define your objectives Determine where you stand vis-a-vis objectives Develpo premises reagrdsing future conditions Analyze alternatives and make a plan Implement the plan and evaluate results What are the benefits of planning Improves focus and flexibility Imporves action orteitation Imporves coordination and control Imporves time management Time Managment Personal time management tips Do say “no” to request that distract you form what you should be doing Dont get bogged down inn details that can be addressed later Do screen telephone calls, emails and meeting request Dont let drop in visitors, text messaging use up your time Do prioritize your important and urgent work Dont become calendar bound by letting other control your schedule Do follow priorities; do most important and urgent work first Some 77% of mangers in one survey said that digital age has increased th number of decisions they have to make 43% said there was less time available to make these decisions Types of plans used by Managers What is teh time horizon Long term vs Short term Long term Look three or more years into teh future Short term plans Typically cover one year or less However: the increasing environmental complexity and dynamism of recent years has severely tested the concept of “long-term” planning Plans are subject to frequent revisions Most executives would likely agree that these complexities adn uncertainties challenge how er actually go about planning and how far ahead we can really plan At the very least we can conclude that there is a lot less permanency to long term plans today and that tey are subject to frequent revision Managment reaeracher Eillot Jaques believes tha people vary in their capability to think with different time horizons Types of Plans used by Managers (3 of 5) Strategic plans Set broad, comprehensive and linger term action directions for teh entire organization or major division Vision Clarifies purpose of the organization and what it hopes to be on the future Typical plans Specify how the organizations resources are used to implement strategy Tactical plans in business often take the form of functional plans Functional plans Incidate how different component within the organiztion will help accompnlish the overall strategy Production plans Finacial plans Facilites Plans Logisitc plans Marketing plans Human Resource Plans Operation plans Describe short-term activities to implement strategic plans Policies: Are standing plans that communicate guidelines for decisions Ex: Policies on office romances: The media is quick to report when a top executive or public figures runs into trouble over an office affair. Are there ant policies on office romances? Employer polices on office raltioshiis vary. One survey find teh following: 24% prohibit relationships among employees in the same department 13% prohibit relationships among employees who have the smae supervisor 80% prohibit relationships between supervisors and subordinates 5% have no restrictions on office romances Procedures: Are rules that describe actions to be taken in specific situations Budgets: are single use plans that commit resources to projects or activities Zero based budgets: allocate resources as if each budget were brand new There is no guarantee that any past funding will be renwer. All propsales, old and new, must compete for available funds at teh start of each new budget cycle Forcasting Attempts to predict the future Qualitaive forecasting uses expert opinions Quantitative forecasting uses mathematical models and statiscal aanylsis of historical data dna surveys Contingency planning Identify alternative course of action to take when things go wrong Anticipate changing conditions Contain trigger points to indicate when to activate plan (or a specific course of action) Scenario planning A long term version of contingency planning Identifying alternative future scenarios Plans made for each future scenario Increases organizations flexibility and preparation for future shocks Benchmarking Use of external and internal comparisons to better evaluate current performance Adopting best practices: things people adn organization do that lead to superior performance Staff Planners Experts who assist in all steps of the planning process They help bring focus and expertise to a wide variety of planning tasks Important: Communication between staff planers landline managers is essential for teh success of teh planning process Goal Setting - Always set SMART goal The solution: Goal Aligment Between Team Leader and Team Member Jonintly plan: Set objectives, set standards, choose actions Individually acy: Perform tasks (member), provide support (leader) Jointly control: Review results, discuss implications, renew cycle x4 Collective effort and commitment Participatroy planning Includes in all planning steps that people who will be affected by the plans adn askedd to help implement them Unloacks motivational potential of goal setting Management by objective (MBO) promotes participation Participation increases understanding and acceptance of plan and commitment to success Participatory planning - Number of people involved in teh decision making process Amazon is intensely focused on what it does. It believes in creating tight single-threaded teams, also known as “2 pizza team.” Data and Decision Making What are some of the important competencies managers must have today? Delegate Marketing and technology Manager must have Technological competency Ability to understand new technologies and to use them to their best advantage Information competency Ability to locate, gather, organize and display information for decision-making and problem solving Analytical competency Ability to evaluate and analyze information to make actual decisions and solve real problems What is the difference between Data and Information Data Raw facts and observation Information Data made useful and meaningful for decision-making Important concepts Big data Exists in huge quantities and is difficult to process without sophisticated mathematical and analytical techniques Data production today Bernard Marr is an internationally best-selling author. He helps organizations improve their business performance, use data more intelligently Data mining The process of analyzing data to produce useful information for decision-makers Management Analytics The systematic evaluation and analysis of data to make informed decision Information drives management Bad Data Refers to information that can be erroneous, misleading, and without general formatting The challenge: Can er use the data that is available in the “Big Data” Needs to be valid Can not trust everything out there Being ethical Look at the trends Data is structured and unstructured Data BIg Data = Structured + Unstructured Information Drive Management decision making What are the characteristics of useful information Easy to access If its credible Accurate Characteristics of useful information: Timely High quality Complete Relevant Understandable What about bad data It's not credible Miss information If it is not structured/ organized Bias based on opinions Confusing If its updated Bad data Refers to information that can be erroneous miss What are some examples of Management information system Business intelligence -BI Information systems to extract and report data in organized ways that are useful to decision-makers Executive dashboards Visually update and display key performance metrics (or Key Performance Indicators -KPIs) and information on a real-time basis Information needs in organization External Environment Information exchanges with the external environment Gather intelligence information Provide public information Information needs within the organizations (internal Enviroement) Information exchange within the organization Facilitate decision making Facilitate problem-solving Managers as information processors Continually gather, share and receive information Now as much electronic as it is face-to-face Always on, always connected How many people telecommute at least once a week 70% of people globally work remotely at least once a week, Work at home after covid 19 our forecast Our best estimate it that 25-30% of the workforce will be working form home multiple days a week by the end of 2021 As of 2023, 12.7% of full time employees work from home, while 28.2% work a hybrid model Managers as problem solvers Problem-solving The process of identifying a discrepancy between actual and desired performance and taking action to resolve it Ishikawa Fishbone diagram To identify the cause of problems Decision A choice among possible alternative courses of action Performance threat Something is wrong or has the potential to go wrong Performance opportunity The situation offers the chance for a better future if the right steps are taken Problem-solving approaches or style - from textbook Problem avoiders Inactive in information gathering and solving problems Problem seekers Proactive in anticipation of problems and opportunities and taking appropriate action to gain an advantage Problem solvers Reactive in gathering information and solving problem Managers - can approach problems in a systematic or intuitive manner Systematic thinking approaches problem in rational, step-by-step and analytical fashion Intuitive thinking approaches problems in a flexible and spontaneous fashion Multidimensional thinking- applies both intuitive and systematic thinking Managers face structured and unstructured problems Structure problems Are ones that are familiar, straight forward, and clear with respect to information needs Program decisions apply solutions that are readily available from past experiences to solve structured problems Know how to solve them Familiar Know what we are dealing with Unstructured problems Are ones that are full of ambiguities and information deficiencies Nonprogrammed decisions apply a specific solution to meet the demands of a unique problem Commonly faced by higher-level management Crisis decision making A crisis involves an unexpected problem that can lead to disaster if not resolved quickly and appropriately Ruled for crisis management Figure out what is going on Remember that speed matters Remember that slow counts, too Respect the danger of the unfamiliar Value the skeptic Be ready to “fight fire with fire” Managers make decisions with various amounts of information Certain environment Offers complete information on possible action alternatives and their consequences Risk environment Lacks complete information but offers probabilities of the likely outcomes for possible action alternatives Uncertain environment Lacks so much information that it is difficult to assign probabilities to the likely outcomes of alternative Ex: Certain and uncertain environments: The worldwide Governance Indicators for over 200 countries, comparing distinct environments (Canada-Brazil) Step 1-Identify and define the problem Focuses on information gathering information processing and deliberation Decision objectives should be established What are some common mistakes in definding problems? Common mistakes in defining problems Defining the problem too broadly or too narrowly Focusing on symptoms instead of causes Choosing the wrong problem to deal with Step 2- Generate and Evaluate Alternative Courses of Action Potential solutions are formulated and more information is gathered, data are analyzed, the advantages and disadvantages of alternative solutions are identified Common mistakes: Abandoning the search for alternatives too quickly Step 3- Decide on a preferred course of Action Two different approaches Behavioural model leads to satisficing decisions Classical model les to optimising decisions Behavioural Model Rationality is bounded because: There are limits our thinks capacity Available information (incomplete) Time constraints Step 4-Implement the decision Involves taking action to make sure the solution decided upon becomes a reality Managers need to have the willingness and ability to implement action plans Problems: Lack of participation error should be avoided Step 5 - Evaluate Results Involves comparing actual and desired results The positive and negative consequences of the chosen course of action should be examined If actual results fall short desire results, the manager returns to earlier steps in the decision-making process At all steps, check ethical reasoning Ask these spotlight questions Utility Does teh decision satisfy all constituents or stakeholders Rights Does the description respect the rights and duties of everyone? Justice Is the decision consistent with the canons of justice Caring Is the decision consistent with my responsibilities to care? Issues in decision-making How do errors happen? Heuristics: are strategies for simplifying decision-making Availability Bias: Bases a decision on recent information or events Representativeness bias: Bases a decision on similarity to other situations Anchoring and Adjustment Bias: Bases a decision on incremental adjustment from a prior decision point Framing error: Tring to solve a problem in the context perceived, positive or negative Confirmation Error: Focusing on information that confirms a decision already made Escalating commitment: Continuing a course of action even though it is not working Creative Decision making Creativity is the generation of a novel idea or unique approach that solves a problem or crafts an opportunity Big C: Creativity occurs when extraordinary things are done by exceptional people Little C: Creativity occurs when average people come up with unique ways to deal with daily events and situations The three types of situational creativity drivers Chapter review What are objectives and goals? The specific results or desired outcomes What are the 5 characteristics of great (SMART) goals? Forecasting - Attempts Qualitative forecasting uses options Quantitative forecasting uses mathematical models and statistical analysis of historical data and surveys Scenarios-Oracle’s crystal ball combines qualitative and quantitative methods

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

In this video we take a look at the 0:02 fetch to code 0:03 execute cycle including its effect on 0:06 the various registers we've previously 0:12 [Music] 0:14 discussed a computer is defined Definition 0:17 as an electronic device that takes an 0:20 input 0:22 processes data 0:25 and delivers output 0:29 in this simple example you can see we're 0:31 taking the input 5 0:35 we're multiplying it by 2 that's our 0:37 process 0:39 and we're outputting 10. 0:44 but this could be way more complex for 0:46 example of a game console 0:48 the input could be the buttons you press 0:50 on a controller 0:53 the processes would then be carried out 0:55 by the console itself 0:59 and the output would be some form of 1:01 update to a monitor 1:02 and sound out for a speaker possibly 1:04 vibration feedback through the 1:06 controller 1:10 to process data a computer follows a set 1:13 of instructions 1:14 known as a computer program 1:18 if we take the lid off a typical desktop 1:20 computer we can identify 1:22 two critical components the memory 1:26 that stores the program and the central 1:29 processing unit or processor 1:31 which is under this large fan and 1:33 carries out the instructions 1:37 a computer carries out its function by 1:40 fetching 1:41 instructions decoding them and then 1:43 executing them 1:44 in a continuous repetitive cycle 1:46 billions of times a second 1:48 let's look at each of these stages in a 1:50 little more detail Fetch 1:53 so let's start with the fetch stage the 1:55 very first thing that happens 1:57 is the program counter is checked as it 2:00 holds the address 2:01 of the next instruction to be executed 2:07 the address stored is then copied into 2:09 the memory address register 2:14 the address is then sent along the 2:16 address bus to main memory 2:18 where it waits to receive a signal from 2:21 the control 2:22 bus so it knows what to do 2:27 as we want to read the data that's 2:29 stored in memory address 2:30 0 0 0 0 the control unit sends 2:34 a read signal along the control bus to 2:36 main memory 2:41 now main memory knows the data needs to 2:44 be read 2:45 the content stored in memory address 000 2:49 can be sent along the data bus to the 2:51 memory data register 2:56 now as we're currently in the process of 2:58 fetching an instruction 3:00 the data received by the memory data 3:03 register gets copied 3:04 into the current instruction register 3:11 the instruction effectively has now been 3:14 fetched from memory 3:16 just before we proceed to the decode 3:18 phase we now 3:19 increment the program counter so that 3:22 the address it contains 3:24 points to the address of the next 3:26 instruction which will need to be 3:30 executed 3:32 the instruction now being held in the 3:33 current instruction register 3:35 is ready to be decoded 3:39 now as we mentioned in the previous 3:41 video the instruction is made up of two 3:43 parts 3:44 we have the op code that's what it is we 3:47 need to do 3:50 and we have the operand what are we 3:53 going to do it to 3:55 now the operand could contain the actual 3:57 data 3:58 or indeed it could contain an address of 4:01 where the data is to be found 4:06 by decoding this instruction we can see 4:08 the operation we need 4:10 is a load operation so we need to load 4:14 the contents of memory location0101 4:18 into the cpus accumulator 4:25 in the exam a simple model will be used 4:27 to describe the 4:29 structure of any given instruction 4:32 you're not going to be expected to 4:34 define how an opcode is made up 4:36 but simply to interpret opcodes in the 4:39 given context of an exam 4:40 question in the example here 4:44 you can see there's a total of 16 4:46 different opcodes available 4:48 and this is because we're using four 4:50 bits for our representation 4:56 so now we've fetched the instruction and 4:59 we've decoded it so we know what we need 5:00 to do 5:01 we're finally ready to execute it 5:05 so we now send address 0101 5:08 to the memory dress register 5:13 now we're in the memory address register 5:15 we can finally send the address 5:18 down the address bus to main memory 5:24 this time we want to read the data 5:26 that's stored in memory 5:28 and so the control unit again sends a 5:30 read signal along the control bus 5:36 so main memories now receive an address 5:38 and a read signal 5:40 so the content stored at memory location 5:43 0101 5:44 can now be sent along the data bus back 5:46 to the cpu 5:47 and into the memory data register 5:54 finally the contents of the memory data 5:56 register are copied to the accumulator 5:59 and this is one of a number of general 6:00 purpose registers found in the cpu 6:04 this first instruction is now complete Branching 6:11 so what does this program actually do 6:14 you should be able to work it through 6:16 carefully and figure it out 6:19 we're now pointing instructions zero 6:21 zero zero one in the program counter 6:23 and we're ready to fetch the second 6:25 instruction 6:27 at the end of this video we're gonna 6:29 provide you with the answer 6:34 so let's talk a second about programs 6:37 that branch 6:40 on the left here we have a very simple 6:42 piece of pseudo code 6:44 line zero says first execute this line 6:46 of code 6:47 line 1 now execute this line and then 6:50 line 2 says 6:52 if the age is greater than 18 then 6:56 we're going to execute lines 3 and 4 6:58 otherwise 6:59 we're going to execute lines six and 7:02 seven 7:03 so this program doesn't necessarily 7:05 follow strictly in sequence from line 7:07 zero through to seven there's a chance 7:10 here the program may branch and jump 7:14 around 7:16 so we're going to pretend that this 7:17 program has been loaded into memory 7:20 each line of code on the left here has 7:23 ended up 7:24 as a location in memory now this is not 7:27 strictly how this would happen in this 7:28 one-to-one way 7:29 but for the purpose of example it's 7:31 absolutely fine 7:35 so the program counter starts by 7:37 pointing to memory address zero 7:39 and we fetch the first instruction 7:41 decode it and execute it 7:44 it then updates and tells us the next 7:47 instruction 7:48 is zero zero zero one because remember 7:50 the program counter is being incremented 7:52 so we fetch it decode it and we execute 7:55 line one of our program 7:59 we then fetch line two which in binary 8:01 is one 8:02 zero 8:06 now at this point depending on what 8:10 happens during the execution 8:11 of line two the program may be required 8:15 to fetch line three from memory or 8:18 line five from memory 8:25 so let's look at how this actually works 8:27 because we've said the program counter 8:28 simply gets incremented 8:31 well in the current instruction register 8:33 we have an instruction with the op code 8:36 0 1 1 0. 8:41 now when we look this up in the decode 8:43 unit we discover that this 8:45 code means branch always 8:51 this replaces the value held in the 8:54 program counter 8:56 with the contents of the operand that's 8:58 the second part of the instruction 9:01 from the current instruction register so 9:03 this case 9:04 one zero zero one 9:09 now when the next fetch cycle begins the 9:12 program counter is obviously checked 9:14 and as its contents have been previously 9:16 updated to a new memory location 9:19 and not simply incremented the program 9:22 effectively is able to jump 9:24 around memory 9:28 so having watched this video you should 9:30 be able to answer the following key 9:32 question 9:33 how does a cpu work 9:39 okay so let's um answer the question we 9:41 posed 9:42 earlier what did that program actually 9:48 do 9:50 so this is the first fetch to code 9:53 execute cycle 9:55 and this is the one that we ran through 9:57 in detail earlier 9:58 it effectively loaded the contents of 10:01 the memory 10:02 stored at location location0101 10:05 into the accumulator in other words 10:08 the dna number 3 is moved 10:11 from memory into the cpu 10:18 we then proceed onto the second fetch 10:20 decode execute cycle 10:23 now this one adds the contents of memory 10:27 located at 0 1 1 0 10:30 to the current contents of the 10:32 accumulator 10:34 so in other words the dna number one 10:38 because that's what's stored at address 10:40 zero one one zero 10:43 is added to the number three that was in 10:45 the accumulator 10:46 the results are stored back over the 10:48 accumulator 10:49 so effectively we've done three plus one 10:53 equals four 10:58 the third fetch to code execute cycle 11:00 stores the contents which are in the 11:02 accumulator 11:03 into memory location zero one one one 11:07 and that's because the op code the first 11:09 part of this current instruction 11:10 zero zero one one is the command to 11:13 store when we look it up in the decoder 11:15 unit 11:16 so in other words the result of the 11:17 previous calculation three plus one 11:19 equals four 11:20 is now written back into main memory 11:28 the fourth fetch decode execute cycle 11:30 outputs the contents of the accumulator 11:33 remember they were copied into main 11:34 memory but they're still held in the 11:35 accumulator 11:37 so in this simple abstraction the number 11:40 four is now 11:41 output to the user so they can see the 11:43 result of the calculation 11:49 the fifth and final fetch code execute 11:51 cycle 11:52 brings a halt to the current program 11:58 so this very simple program which has 12:01 five 12:02 fetch decode execute cycles has 12:04 performed the calculation 12:06 three plus one is then stored the result 12:09 in main memory 12:10 and displayed the result four to the 12:12 user 12:13 and in a high-level language this may 12:15 look something very similar to the 12:17 following two lines of code 12:20 sum variable equals num1 plus num2 12:24 print sum to the user 12:27 so you can start to get an appreciation 12:29 here of how the high level code you 12:32 write actually ends up being fetched 12:34 decoded 12:35 and executed inside a processor 12:38 of course your processor is doing 12:40 billions and billions of these 12:42 operations a second 12:43 which when you think about it is really 12:45 very impressive 12:52 [Music] 13:03 you. make 10 questions for a standerd of a level

Create me a multiple choice test questions with 4 options on the following topic:Consumer Education for Different Audience 1. Children and Youth: - Focus: Building foundational knowledge about basic consumer concepts, making safe choices, understanding money and value, and recognizing scams and unsafe situations. 2. Teens and Young Adults: - Focus: Building financial literacy, responsible debt management, understanding contracts and agreements, responsible technology use, online safety, and consumer rights. 3. Working Adults and Families: - Focus: Managing budgets, making informed purchasing decisions, understanding credit and debt, finding consumer protection resources, and navigating complex financial products (mortgages, insurance, investments). 4. Seniors: - Focus: Protecting themselves from scams and fraud, understanding common consumer issues like telemarketing, identity theft, and online scams, managing medications and healthcare costs, and accessing community resources. 5. Special Populations: - Focus: Adapting consumer education programs to the specific needs of people with disabilities, immigrants, refugees, and other marginalized communities. 6. Business and Industry:- Focus: Understanding ethical marketing practices, complying with consumer protection laws, and providing clear and accurate information to consumers. 7. Policymakers and Regulators: - Focus: Understanding consumer needs, developing effective consumer protection laws, enforcing regulations, and ensuring a fair and competitive marketplace. Adapting consumer education programs for children, teens, and seniors requires tailoring content and delivery methods to their unique needs and learning styles. Children (Ages 5-12): - Understanding the concept of money: Teaching children about saving, spending, and the value of money. - Developing basic budgeting skills: Helping children learn to make choices about how to spend their allowance or pocket money. EFFECTIVE STRATEGIES •Focus on basic concepts: Introduce core concepts like saving, spending, and budgeting in a fun and engaging way. Use simple language and relatable examples. •Real-life scenarios: Use age-appropriate scenarios to illustrate financial concepts, like buying toys or snacks. •Parental involvement: Encourage parent participation and provide resources to help them reinforce lessons at home. Teens (Ages 13-18): - Building budgeting and financial planning skills: Teaching teens how to manage their money, set financial goals, and plan for the future. - Navigating the digital marketplace: Equipping teens with the knowledge and skills to make safe and informed online purchases, understand digital marketing, and protect themselves from scams. EFFECTIVE STRATEGIES • Practical skills: Focus on skills relevant to teens, like managing money for social activities, saving for college, and understanding credit cards. • Digital literacy: Address the growing influence of online shopping, social media advertising, and financial scams. • Real-world applications: Connect financial concepts to real-life decisions teens make, like choosing a part-time job or making purchases online. Seniors (Ages 65+) - Managing retirement savings and healthcare costs: Providing information and resources on retirement planning, Medicare and Medicaid, and other healthcare options. - Navigating the digital world: Offering technology training and resources to help seniors access online services and information safely and securely. EFFECTIVE STRATEGIES • Addressing specific concerns: Focus on topics relevant to senior citizens, like retirement planning, managing healthcare expenses, and avoiding scams. • Clear and concise communication: Use simple language and visual aids to ensure easy understanding. • Social interaction: Create opportunities for seniors to share experiences and learn from each other. Teaching Financial Literacy in school and Communities In Schools: Curriculum Integration: Financial literacy concepts can be seamlessly integrated into existing subjects, making learning more relevant and engaging. - Math: Budgeting exercises, calculating interest rates, analyzing financial data, and understanding compound interest are all natural applications of math skills. - Social Studies: Exploring the history of money, financial institutions, economic systems, and the impact of financial decisions on society provide valuable context. - Economics: Discussions about supply and demand, inflation, investment, and the role of consumers in the economy enhance financial literacy. Dedicated Courses: Offering elective courses or workshops specifically focused on personal finance provides deeper dives into crucial topics. - Personal Finance: Cover budgeting, saving, investing, credit, debt management, and insurance. - Entrepreneurship: Introduce concepts like business planning, marketing, financial forecasting, and managing cash flow. In Communities: Community Centers and Libraries: Workshops, seminars, and classes tailored to adults and families provide accessible learning opportunities. - Financial Planning: Cover budgeting, retirement planning, debt management, and estate planning. - Homeownership: Provide guidance on buying, selling, and maintaining a home. - Consumer Protection: Educate individuals about their rights and how to avoid scams. Partnerships with Financial Institutions: Collaborations with banks, credit unions, and financial advisors offer valuable resources, workshops, and financial literacy programs. Consumer Education for Low-Income and Vulnerable Populations Low-income refers to individuals or households with limited financial resources, typically below a certain threshold. Low-income individuals may face challenges like: 1. Limited education and job opportunities 2. Poor living conditions and housing 3. Food insecurity and malnutrition Causes of low income: 1. Unemployment or underemployment 2. Low-paying jobs or minimum wage 3. Limited education or skills 4. Single parenthood or large family size Vulnerable population'' is a term that is used to describe a group of people who possess some sort of disadvantage. elderly people, people with low incomes, homeless people, people in prison, migrant workers, pregnant women, Family Consumer Education: Managing Household Finances and Resources Financial literacy is the ability to understand and manage personal finances effectively. 1. Debt Debt is money you spend that isn’t yours. If you borrow money from the bank, use a credit card, or take out a short-term loan, or a payday loan, you are accumulating debt. Good debt is considered money borrowed for things that are absolutely necessary for making a life e.g. a house and for advancing your money-making potential e.g. an education. Bad debt is considered borrowing money or using a credit card to pay for things you don’t need, such as expensive clothes, hi-tech electronics, eating out at restaurants, going on holidays, etc. 2. Saving Saving is an essential part of financial wellness, a secure present, and a happy future. 3. Budgeting Budgeting is the life skill of planning and managing your money. By understanding exactly where your money goes every month, you are empowered to create an actionable plan by which you can spend less, by curtailing those unnecessary expenses and saving more for the things you need and want. 4. Investing Investing is all about creating and growing the wealth you need to enjoy a financially secure and happy future. It’s about putting your money into something that will make you a profit over time, such as property, retirement funds, and unit trusts Integrating Consumer Education into the Home Economics Curriculum. Integrating consumer education into the home economics curriculum can provide students with essential skills for making informed choices about their personal finances, food, clothing, and overall well-being. Here are some strategies and ideas for effectively incorporating consumer education: Financial Literacy Budgeting: Teach students how to create and manage a personal budget, including setting financial goals, tracking expenses, and understanding savings. Saving and Investment: Cover the basics of saving, including different saving accounts, and introduce concepts related to investing. Food and Nutrition Food Label Literacy: Engage students in learning how to read and interpret food labels, including nutrition facts and ingredient lists. Grocery Shopping Skills: Teach students how to compare product costs, understand unit pricing, and make healthy, budget-friendly choices while shopping. Clothing and Textile Education Consumer Choices in Clothing:Discuss factors influencing clothing purchases, such as quality, price, and sustainability. Fashion and Trends: Analyze the impact of marketing and advertising on consumer behavior regarding clothing. Sustainable Purchasing Eco-Friendly Choices: Raise awareness about environmentally friendly products and the importance of sustainability in consumer choices. Project-Based Learning - Assign real-life projects where students must apply their knowledge, such as creating a meal plan within a budget, planning a shopping list based on nutrient needs, or evaluating the cost-effectiveness of different products. Technology Integration - Use technology to teach students about online shopping, price comparison websites, and apps that aid budgeting and financial planning. Collaborative Learning Opportunities - Organize team projects where students work together to solve consumer-related problems, emphasizing teamwork and communication skills. Assessment and Reflection - Incorporate assessments that allow students to reflect on what they have learned about consumer education and how they can apply these skills in their daily lives.

Understanding the Features of Finance: A Guide for Newbies Finance is a broad field that involves managing money, including activities such as investing, borrowing, lending, budgeting, saving, and forecasting. As a beginner, understanding the basic features of finance is crucial. This guide will relate these features to blockchain technology, cryptocurrency, and decentralized finance (DeFi). 1. Basic Financial Concepts Investing: Putting money into assets like stocks, bonds, or real estate with the expectation of earning a return. In the blockchain world, this translates to investing in cryptocurrencies like Bitcoin, Ethereum, or various DeFi projects. Borrowing and Lending: Traditional finance involves banks and financial institutions providing loans. In the DeFi space, platforms like Aave and Compound allow users to borrow and lend cryptocurrencies without intermediaries. Budgeting: Planning how to allocate your income to cover expenses, save, and invest. Using blockchain technology, you can utilize smart contracts to automate budgeting and savings processes. 2. Blockchain Technology Blockchain is a decentralized ledger that records transactions across multiple computers. It is the technology behind cryptocurrencies and has several key features: Transparency: All transactions are recorded on a public ledger, making them visible to anyone. Security: Cryptographic techniques ensure that data on the blockchain is secure and tamper-proof. Decentralization: No single entity controls the blockchain, reducing the risk of centralized control and failure. 3. Cryptocurrencies Cryptocurrencies are digital or virtual currencies that use cryptography for security. They operate on blockchain technology and offer several advantages: Lower Transaction Costs: Sending money across borders is cheaper with cryptocurrencies compared to traditional banking methods. Accessibility: Anyone with an internet connection can access cryptocurrencies, promoting financial inclusion. Ownership and Control: Users have complete control over their funds without relying on banks. 4. Decentralized Finance (DeFi) DeFi is a movement that uses blockchain technology to recreate and improve traditional financial systems in a decentralized manner. Key features of DeFi include: Smart Contracts: Self-executing contracts with the terms directly written into code, enabling trustless and automated transactions. Liquidity Pools: Users can provide their assets to a pool and earn interest or rewards, promoting liquidity in the DeFi ecosystem. Yield Farming: A strategy where users move their assets between different DeFi platforms to maximize returns. 5. Applications in DeFi and Blockchain HaloFi Save: A platform that leverages blockchain technology to help people save money efficiently and securely. It encourages users to save larger amounts for longer durations, offering higher interest rates compared to traditional banks. Non-Custodial Savings: Users have full control over their funds, reducing the risk of losing their money to institutional failures or fraud. Access to DeFi: Integrating with DeFi platforms like Moola Market, HaloFi Save provides additional opportunities to earn interest on savings, promoting financial growth and stability. Practical Example: A Farmer's Journey Imagine a farmer in a remote village in Africa. Traditionally, this farmer might not have access to banking services, making it difficult to save money, get loans, or invest in better farming equipment. With platforms like HaloFi Save, the farmer can: Save money securely and earn interest. Access microloans through DeFi platforms integrated with Celo. Participate in educational programs to learn more about blockchain and DeFi. Conclusion Blockchain technology, through platforms like HaloFi Save and initiatives by Celo Africa DAO, has the potential to drive significant social change by promoting financial inclusion, transparency, and access to resources. By empowering individuals and communities with the tools and knowledge to participate in the digital economy, blockchain can help address global issues and foster sustainable development.

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