How to Lie with Statistics” by Darrell Huff was a great book to read for a student like myself that is entering a course in statistics. It gave me the insight that I needed to know what statistics is all about and even the ‘tricks’ about using statistics that I can use when I get older and maybe have an important business job for example were I must present for the company and this book proves to be my savior. Though anyway it’s still very influential. This book wasn’t very hard to get through and it gave me a new outlook on statistics that I definitely didn’t have prior to my reading it.

Before I may have had my own interpretation of statistics, but Huff has shaped my understanding into something much better than it was before. Something that the book was big on was common error in statistics that a lot of people come a crossed. And something that I learned is that these errors are not always unintentional. Sometimes, in fact, they can actually be intentionally done. Huff shows us how some of the simple ideas such as averages are

manipulated to be more appealing to the viewer. Even how the mode can be the most frequently observed outcome even though it is rarely reported with numeric data.

One of the biggest ideas shown in the book to my opinion was how he explained the power of the graph. Eye-catching graphs can really make someone see what you want them to see and perhaps hide the bad. One thing is the choice of ranges on graphs can have huge impact on interpretation. Depending on what you set your range to the graph could prove to show completely opposite ideas. By truncating the bottom of a line or bar chart, so that differences seem larger than they are, can be the difference on a project depending on what you are trying to illustrate on the graph. The next big thing is that the choice of proportion of y-axis to x-axis can distort your perspective as well and it is very easy to do with modern software in today’s world. That one change with...

...Statistics 1
Business Statistics
LaSaundra H. – Lancaster
BUS 308 Statistics for Managers
Instructor Nicole Rodieck
3/2/2014
Statistics 2
When we hear about business statistics, when think about the decisions that a manager makes to help make his/her business successful. But do we really know what it takes to run a business on a statistical level? While some may think that businessstatistics is too much work because it entails a detailed decision making process that includes calculations, I feel that without educating yourself on the processes first you wouldn’t know how to imply statistics. This is a tool managers will need in order to run a successful business. In this paper I will review types of statistical elements like: Descriptive, Inferential, hypothesis development and testing and the evaluation of the results. Also I will discuss what I have learned from business statistics.
My description of Descriptive statistics is that they are the numerical elements that make up a data that can refer to an amount of a categorized description of an item such as the percentage that asks the question, “How many or how much does it take to “ and the outcome numerical amount. According to “Dr. Ashram’s Statistics site” “The quantities most commonly used to...

...How to Lie with StatisticsSummary
There are some people that rely heavily on the statistical information provided by the media, government, and other research groups in order to form opinions or come to a conclusion on a particular idea or product. However they fail to realize that a lot of the time the data is manipulated in such a way that leads them to believe something that is not actually the case. Statistics can lie in many ways the first way is by using a sample that has a bias. For instance, the data collected would only be of one particular group of people, but they would claim it was the population. Another way data is manipulated is through averages. The data will be presented as the average, but the type of average that is taken is not given. For example is it the arithmetical average, median, or mode that is being used to present the data. This can completely skew the data one way or another. Furthermore, when data is presented the presenter can lie by leaving out certain things that will usually go unnoticed by the reader. In addition, many people make a big deal about something that doesn’t matter when using statistics, which leads the reader to believe that whatever the made a big deal about actually is significant. There could be a difference that is so tiny that it doesn’t have importance, however leaving out the range of error could also...

...Catherine Davison
October 22, 2013
Summary #4
Chapter 8 – Post Hoc Rides Again
The Post – hoc analysis which is the cause and effect problem.
Methods of presenting cause and effect: 1) present a result without a significance value 2) use untestable assumptions 3) use precision and accuracy interchangeably 4) perform nonsensical test that sound good.
Keep in mind that a statistic is only worthwhile when it satisfies the assumptions on the test. Knowing whether the assumptions are met is dependent on the competence of the person running the test.
Just because two things seem to have a relationship, could it have been by pure chance? It cannot be determined by causation and effect. The two variables have no effect on each other at all.
Chapter 9 – How to Statisticulate
Statisticulate is the process of misleading people using statistics. It is also misinforming with figures, or statistical manipulation might not be a mathematician purpose.
Lying with statistics – is this dishonesty or incompetence? Mostly dishonesty.
The author list various tricks – things like measuring profit on cost price, showing a graph with a finer Y-axis scale just to show the steep growth is, how income calculations mislead by involving children in the family as individuals for the average amongst a few.
Chapter 10 – How to Talk Back to a Statistic
In...

...A Synopsis of How to Lie with Statistics by Darrell Huff
When most people hear or read a statistic, they quickly have to decide if the numbers listed are valid or invalid. It is usually assumed that the author of the statistic is knowledgeable in the field to which the statistic pertains. However, on many occasions, the statistic is false, due to the author’s wording. Darrell Huff’s novelHow to Lie with Statistics is a manual that can help individuals catch these lies. The novel allows readers to solve marketing ploys and dismiss certain statistics as faulty.
The first chapter focuses on bias. The book states that all statistics are based on samples, and these samples have bias. This means that no matter what the reader will have a biased opinion. This bias is spawned from the respondents replying dishonesty, the author choosing a sample that gives better results, and the availability of data. Huff uses a survey of readership of two magazines, which had refuting results. This is because, due to the readers’ personal biases, they answered the survey dishonestly. This example closes the chapter, teaching readers to always assume that the sample has a bias. The second chapter focuses on averages. It states that there are actually three types of averages: mean, median, and mode. Mean is the arithmetic...

...How to Lie with Statistics Book Summary
The book How to Lie with Statistics written by Darrell Huff shows you howstatistics are used to mislead; sometimes unintentionally, other times on purpose. It gives the readers the knowledge necessary to intelligently question and understand the story behind the numbers. In other words, it shows the tricks the crooks use, so that honest men can use this knowledge for self defense.
I think it’s particularly useful for a manager or an executive to read and understand this book, because they are usually presented with a lot of numbers, graphs and charts and are expected to make decisions based on these numbers. People collecting and presenting the numbers to management could employ some of the tricks explained in this book and therefore, we should be careful when basing our decisions on those numbers.
It’s interesting that although this book was written in 1954, the concepts explained are just as pertinent today. Some salary figures seem to be outdated but the tricks remain pretty much the same.
The book starts with explaining the importance of sample selection and built-in bias. Sampling is critical in statistics because we can’t always count or observe every item in a population and therefore have to base our judgments on a selected sample. However, a sample with a built-in bias...

...Executive Summary Business Statistics
In this assignment I compiled the data of the Nissan GT-R 3.8 (R35). The Data collected includes the age, type and price which allowed me to make a statistic about how the age affects the price of this certain model over several years. I will be using the correlation regression and scatter diagram to get the regression line. As we can see, the price drops the elder the car is. Inside the range of the diagram the prediction might be accurate. So we can tell very precisely how much the car is going to cost in the next few years, but we won’t be able to give a very precise prediction on how much the car is going to cost after a long term (more than 10 years). This project shows the readers to justify why the age of the car affects the price of the car. The results vary for different cars but ultimately, the results that were found in this assignment are valuable in terms of understanding the automobile market.
Table of Content
1.0 Correlation and Regression
Generally, regression locates bivariate data in terms of a mathematical relationship, able to be graphed as a line or curve while correlation describes the nature of the spread of the items about the line or curve. (Francis, p.165, 2004)
Regression is concerned with obtaining a mathematical equation which describes the relationship between two variables. The equation can be used for a...

...scheduling and network models.
Chapter 1 illustrates a number of ways to summarise the information in data sets, also known as
descriptive statistics. It includes graphical and tabular summaries, as well as summary measures
such as means, medians and standard deviations.
Uncertainty is a key aspect of most business problems. To deal with uncertainty, we need a basic
understanding of probability. Chapter 2 covers basic rules of probability and in Chapter 3 we
discuss the important concept of probability distributions in some generality.
In Chapter 4 we discuss statistical inference (estimation), where the basic problem is to estimate
one or more characteristics of a population. Since it is too expensive to obtain the population
information, we instead select a sample from the population and then use the information in the
sample to infer the characteristics of the population.
In Chapter 5 we look at the topic of regression analysis which is used to study relationships
between variables.
In Chapter 6 we study another type of decision making called decision analysis where costs and
proﬁts are considered to be important. The problem is not whether to accept or reject a statement
but to select the best alternative from a list of several possible decisions. Usually no statistical
data are available. Decision analysis is the study of how people make decisions, particularly
when faced with imperfect...

...Random Sampling Method. In this case study, Mr Kwok collected a random sample of 1000 flights and proportions of three routes in the sample. He divides them into different sub-groups such as satisfaction, refreshments and departure time and then selects proportionally to highlight specific subgroup within the population. The reasons why Mr Kwok used this sampling method are that the cost per observation in the survey may be reduced and it also enables to increase the accuracy at a given cost.
TABLE 1: Data Summaries of Three Routes
Route 1
Route 2
Route 3
Normal(88.532,5.07943)
Normal(97.1033,5.04488)
Normal(107.15,5.15367)
SummaryStatistics
Mean
88.532
Std Dev
5.0794269
Std Err Mean
0.2271589
Upper 95% Mean
88.978306
Lower 95% Mean
88.085694
N
500
Sum
44266
SummaryStatistics
Mean
97.103333
Std Dev
5.0448811
Std Err Mean
0.2912663
Upper 95% Mean
97.676525
Lower 95% Mean
96.530142
N
300
Sum
29131
SummaryStatistics
Mean
107.15
Std Dev
5.1536687
Std Err Mean
0.3644194
Upper 95% Mean
107.86862
Lower 95% Mean
106.43138
N
200
Sum
21430
From the table above, the total number of passengers for route 1 is 44,266, route 2 is 29,131 and route 3 is 21,430 and the total numbers of passengers for 3 routes are 94,827....

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