Chapter 1 what is statistics
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This document outlines the syllabus for MST 103 - Introduction to Statistics and Probability, a 3-credit course taught on Sundays from 9-11 am by Professor Ahmed HassenYoussef. The textbook is Statistical Techniques in Business and Economics with Global Data sets. The goals of the course are to understand descriptive and inferential statistics, distinguish between variable types, and understand the four levels of measurement. Statistics is defined as the science of collecting, organizing, analyzing, and interpreting numerical data to assist with decision making.
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The Standard NormalDistribution and z ScoresKeren SuCorbisC.docxirened6 The Standard NormalDistribution and z Scores
Keren Su/Corbis
Chapter Learning Objectives
After reading this chapter, you should be able to do the following:
1. Identify the characteristics of the standard normal distribution.
2. Demonstrate the use of the z transformation.
3. Determine the percent of a population above a point, below a point, andbetween two points on the horizontal axis of a normal distribution.
4. Calculate z scores using Excel.
5. Describe alternative standard scores.
6. Demonstrate the use of the modified standard score.
Introduction
The data that describe characteristics of groups come from either samples or populations,explained in the first two chapters. By way of reminder, recall that populations include allpossible members of any specified group. All university students, all psychology majors, allresidents of Orange County, and all left-handed male tennis players in their 20s are eachdescriptions of a population. We rely on Greek letters, such as µ for the mean and σ for thestandard deviation, to distinguish population parameters from the statistics that describesamples. (The word parameter indicates a characteristic of a population.) Remove one ormore individuals from any population, and the resulting group is a sample.
As we were describing populations, we noted that some are “normally distributed.” Thesecharacteristics indicate normality: (a) data distributions are symmetrical, (b) all the measuresof central tendency have very similar values, and (c) the value of the standard deviation isabout one-sixth of the range.
Data normality does not simply mean that the frequency distribution will appear as a bell-shaped curve; it means that predictable proportions of the entire population will occur inspecified regions of the distribution, and this holds for all normal data distributions. Forexample, the region under a normal curve from the mean of the population to one standarddeviation below the mean always includes 34.13% of the area under the curve. Because normal distributions are symmetrical, from the mean to one standard deviation above themean also includes 34.13%, so from +1σ or −1σ includes about 68.26% of the area under thecurve in any normally distributed population. As long as the data are normally distributed,those percentages hold true. Since many mental characteristics are normally distributed,researchers can know a good deal about such a characteristic without actually gathering thedata and doing the analysis. Whether the characteristic is intelligence, achievementmotivation, anxiety, or any other normally distributed characteristics, the proportion of thedistribution within +1 or −1 standard deviation from the mean will be the same:
· If a particular intelligence scale has µ = 100 and σ = 15, about 68% of any generalpopulation will have intelligence scores between 85 and 115.
· Likewise, if an achievement motivation scale has µ = 40 and σ = 8, about 2/3 of anypopulation will have achievement motivation s.
INFERENTIAL STATISTICS: AN INTRODUCTION
INFERENTIAL STATISTICS: AN INTRODUCTIONJohn Labrador For instance, we use inferential statistics to try to infer from the sample data what the population might think. Or, we use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance in this study.
Statistics and probability 
Statistics and probability Muhammad Mayo This document provides an overview of statistics and probability as taught in a lecture. It begins by defining statistics as the science of drawing conclusions about phenomena from sample data. Some key points:
- Statistics has many applications across various disciplines.
- The course will cover descriptive statistics, probability, and inferential statistics over 15 lectures.
- Students will complete homework assignments and take midterm and final exams to be graded on their understanding.
- The goal is for students to learn statistical techniques to make data-driven decisions in their fields of study.
Statistics and probability lecture 01
Statistics and probability lecture 01MuhammadTufailKaran The document provides an introduction to statistics and probability. It discusses key concepts including population and sample, types of data and variables, measurement scales, errors in measurement, and statistical inference. The objectives are to develop a statistical thinking approach and teach basic statistical techniques. The course will cover descriptive statistics, probability, and inferential statistics over 15 lectures. Students will complete homework assignments and exams.
assignment of statistics 2.pdf
assignment of statistics 2.pdfSyedDaniyalKazmi2 This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docx
Topic Learning TeamNumber of Pages 2 (Double Spaced)Num.docxAASTHA76 Topic: Learning Team
Number of Pages: 2 (Double Spaced)
Number of sources: 1
Writing Style: APA
Type of document: Essay
Academic Level:Master
Category: Psychology
VIP Support: N/A
Language Style: English (U.S.)
Order Instructions:
I will attach the instruction. On this paper please follow the instructions carefully. Thank you
Correlation
PSYCH/610 Version 2
1
University of Phoenix Material
Correlation
A researcher is interested in investigating the relationship between viewing time (in seconds) and ratings of aesthetic appreciation. Participants are asked to view a painting for as long as they like. Time (in seconds) is measured. After the viewing time, the researcher asks the participants to provide a ‘preference rating’ for the painting on a scale ranging from 1-10. Create a scatter plot depicting the following data:
Viewing Time in Seconds
Preference Rating
10
3
12
4
24
7
5
3
16
6
3
4
11
4
5
2
21
8
23
9
9
5
3
3
17
5
14
6
What does the scatter plot suggest about the relationship between viewing time and aesthetic preference? Is it accurate to state that longer viewing times are the result of greater preference for paintings? Explain. Submit your scatter plot and your answers to the questions to your instructor.
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES.In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements ab.
Sta301 lec01
Sta301 lec01Rizwan Alvi The document provides an overview of statistics and probability. It defines statistics as the science used to draw conclusions from real data collected through sampling. The document outlines the wide variety of fields that use statistics and describes the nature of the discipline as involving descriptive and inferential statistics. It also provides details on the course structure, learning objectives, exams, and grading.
Illustrating Random Variables and Distinguishing Between a Discrete.pptx
Illustrating Random Variables and Distinguishing Between a Discrete.pptxbinoy ly This document discusses random variables and the difference between discrete and continuous random variables. It defines a random variable as a numerical quantity generated by a random experiment. A random variable is discrete if it has a finite or countable number of possible values, while a continuous variable contains values within a whole interval of numbers. Examples are provided of discrete random variables like the number of heads in a coin flip, and continuous variables like height. Statistics is defined as using data to make decisions by collecting, analyzing, and summarizing data to answer questions.
Math Statistics Essay
Math Statistics EssayThesisPapersForSaleM This document discusses using inferential statistics to analyze the issue of obesity among people. Data from a 2000 study on the connection between obesity and health-related quality of life in individuals aged 18 and older is analyzed. After controlling for socioeconomic factors, the study found obesity to be significantly associated with poorer health. Obesity in both children and adults is linked to increased risk of diseases such as heart disease, diabetes, and high blood pressure. Childhood obesity can also negatively impact physical and psychological well-being.
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docx
Page 266LEARNING OBJECTIVES· Explain how researchers use inf.docxkarlhennesey Page 266
LEARNING OBJECTIVES
· Explain how researchers use inferential statistics to evaluate sample data.
· Distinguish between the null hypothesis and the research hypothesis.
· Discuss probability in statistical inference, including the meaning of statistical significance.
· Describe the t test and explain the difference between one-tailed and two-tailed tests.
· Describe the F test, including systematic variance and error variance.
· Describe what a confidence interval tells you about your data.
· Distinguish between Type I and Type II errors.
· Discuss the factors that influence the probability of a Type II error.
· Discuss the reasons a researcher may obtain nonsignificant results.
· Define power of a statistical test.
· Describe the criteria for selecting an appropriate statistical test.
Page 267IN THE PREVIOUS CHAPTER, WE EXAMINED WAYS OF DESCRIBING THE RESULTS OF A STUDY USING DESCRIPTIVE STATISTICS AND A VARIETY OF GRAPHING TECHNIQUES. In addition to descriptive statistics, researchers use inferential statistics to draw more general conclusions about their data. In short, inferential statistics allow researchers to (a) assess just how confident they are that their results reflect what is true in the larger population and (b) assess the likelihood that their findings would still occur if their study was repeated over and over. In this chapter, we examine methods for doing so.
SAMPLES AND POPULATIONS
Inferential statistics are necessary because the results of a given study are based only on data obtained from a single sample of research participants. Researchers rarely, if ever, study entire populations; their findings are based on sample data. In addition to describing the sample data, we want to make statements about populations. Would the results hold up if the experiment were conducted repeatedly, each time with a new sample?
In the hypothetical experiment described in Chapter 12 (see Table 12.1), mean aggression scores were obtained in model and no-model conditions. These means are different: Children who observe an aggressive model subsequently behave more aggressively than children who do not see the model. Inferential statistics are used to determine whether the results match what would happen if we were to conduct the experiment again and again with multiple samples. In essence, we are asking whether we can infer that the difference in the sample means shown in Table 12.1 reflects a true difference in the population means.
Recall our discussion of this issue in Chapter 7 on the topic of survey data. A sample of people in your state might tell you that 57% prefer the Democratic candidate for an office and that 43% favor the Republican candidate. The report then says that these results are accurate to within 3 percentage points, with a 95% confidence level. This means that the researchers are very (95%) confident that, if they were able to study the entire population rather than a sample, the actual percentage who preferred th ...
Complete the Frankfort-Nachmias and Leon-Guerrero (2018) SPSS®.docx
Complete the Frankfort-Nachmias and Leon-Guerrero (2018) SPSS®.docxbreaksdayle Complete
the Frankfort-Nachmias and Leon-Guerrero (2018) SPSS® problems and chapter exercises listed below.
Ch. 6: Chapter Exercises 2, 4, 6, 8, and 12
Ch. 7: SPSS® Problem 2
Ch. 7: Chapter Exercises 2, 4, 6, 8, and 12
Include
your answers in a Microsoft® Word document.
Click
the Assignment Files tab to upload your assignment.
Please see Chapter 6 material.
Sampling and Sampling Distributions
Chapter Learning Objectives
Describe the aims of sampling and basic principles of probability
Explain the relationship between a sample and a population
Identify and apply different sampling designs
Apply the concept of the sampling distribution
Describe the central limit theorem
Until now, we have ignored the question of who or what should be observed when we collect data or whether the conclusions based on our observations can be generalized to a larger group of observations. In truth, we are rarely able to study or observe everyone or everything we are interested in. Although we have learned about various methods to analyze observations, remember that these observations represent a fraction of all the possible observations we might have chosen. Consider the following research examples.
Example 1:
The Muslim Student Association on your campus is interested in conducting a study of experiences with campus diversity. The association has enough funds to survey 300 students from the more than 20,000 enrolled students at your school.
Example 2:
Environmental activists would like to assess recycling practices in 2-year and 4-year colleges and universities. There are more than 4,700 colleges and universities nationwide.
1
Example 3:
The Academic Advising Office is trying to determine how to better address the needs of more than 15,000 commuter students, but determines that it has only enough time and money to survey 500 students.
The primary problem in each situation is that there is too much information and not enough resources to collect and analyze it.
Aims of Sampling
2
Researchers in the social sciences rarely have enough time or money to collect information about the entire group that interests them. Known as the
population
, this group includes all the cases (individuals, groups, or objects) in which the researcher is interested. For example, in our first illustration, there are more than 20,000 students; the population in the second illustration consists of 4,700 colleges and universities; and in the third illustration, the population is 15,000 commuter students.
Population
A group that includes all the cases (individuals, objects, or groups) in which the researcher is interested.
Fortunately, we can learn a lot about a population if we carefully select a subset of it. This subset is called a
sample
. Through the process of
sampling
—selecting a subset of observations from the population—we attempt to generalize the characteristics of the larger group (population) based on what we learn from the smaller group (t ...
03 PSBE3_PPT.Ch03.ppt
03 PSBE3_PPT.Ch03.pptBishoyRomani The document discusses key concepts in designing samples and experiments, including:
1) Random sampling methods like simple random samples and stratified samples produce unbiased estimates, while convenience samples can be biased.
2) Experiments aim to isolate the effect of treatments by using random assignment to control for lurking variables. Completely randomized designs randomly assign subjects to treatments.
3) Increasing sample sizes reduces sampling variability and produces estimates closer to the true population parameters, assuming a random sample is used. Sample size matters more than population size for reducing variability.
Statistics and Public Health. Curso de Inglés Técnico para profesionales de S...
Statistics and Public Health. Curso de Inglés Técnico para profesionales de S...Universidad Particular de Loja
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Chapter 1 what is statistics
- 1. MST 103 – Introduction to Statistics and Probability Probability and Statistical Pro. f Ahmed HassenYoussef Department of Applied Statistics, Institute of Statistics, Cairo University
- 2. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability COURSE TITLE: Introduction to Statistics And ProbabilityCOURSE NUMBER: MST 103 NUMBER OF CREDITS: 3
- 3. CLASS TIME AND PLACE: Sunday 9:00 – 11:00 am; Loc:B2.2 MST 103 – Introduction to Statistics and Probability Textbook Statistical Techniques in Business and Economics with Global Data sets By Douglas A. Lind William G. Marchal Samuel A. Wathen McGrow- Hill, Irwin 2008
- 4. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability GOALS Understand why we study statistics.
- 5. Explain what is meant by descriptive statistics and inferential statistics.
- 6. Distinguish between a qualitative variable and a quantitative variable.
- 7. Describe how a discrete variable is differentfrom a continuous variable.
- 8. Distinguish among the nominal, ordinal, interval, and ratio levels of measurement. Prof. Ahmed Hassen
- 9. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability What is Meant by Statistics? Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting numerical data to assist in making More Effective DecisionsProf. Ahmed Hassen Prof. Ahmed Hassen
- 10. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability A few headlines 70 Percent Chance That Lare g Earthuake q Will Strike San Francisco By 2030. 10/15/99, Geological Survey
- 11. If Hurricane Ivan Hits U.S., 2004 Would Be A '1 In 50 Year Series Of Storm EventsFor Insurance Industry. EQECAT Prof. Ahmed Hassen Prof. Ahmed Hassen
- 12. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Who Uses Statistics? Prof. Ahmed Hassen
- 13. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Population versus Sample A population is a collection of all possible individuals, objects, or measurements of interest. A sample is a portion, or part, of the population of interest Prof. Ahmed Hassen Prof. Ahmed Hassen
- 14. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Quantitative Variables - Classifications Quantitative variables can be classified as either discrete or continuous. Discrete variables: can only assume certain values and there are usually “gaps” between values. EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,...,etc). Continuous variable can assume any value within a specified range. EXAMPLE: The pressure in a tire, the weight of meat, or the height of students in a class. Prof. Ahmed Hassen
- 15. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Four Levels of Measurement Nominal level –data that is ClassifiedInto Categoriesand cannot be arranged inany particular order. EXAMPLES: eye color, gender, religious affiliation. Interval level - similar to the ordinal level, with the additional property that meaningfulamounts of differences between data values can be determined. There is no natural zero point. EXAMPLE: Temperature on the Fahrenheit scale. Ordinal level – involves data arranged in some order, but the differences between data values Cannot Be Determined Or Are meaningless. EXAMPLE: During a taste test of 4 soft drinks, Coca Cola was ranked number 1, Sprite number 2, Seven-up number 3, and Pepsi number 4. Ratio level- the interval level with an inherent zero starting point. Differences and ratios are Meaningful For This LevelOfmeasurement. EXAMPLES: Monthly income of surgeons, or distance traveled by manufacturer’s representatives per month. Prof. Ahmed Hassen
- 16. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Questions of Chapter 1 1. A population is a collection of all individuals, objects, or measurements of interest. True/False Answer: True Prof. Ahmed Hassen
- 17. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Questions of Chapter 1 3. There are four levels of measurement qualitative, quantitative, discrete, and continuous. True/False Answer: False Prof. Ahmed Hassen Prof. Ahmed Hassen
- 18. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Questions of Chapter 1 5. The CIA World Fact book cited these numbers for the U . S: Birthrate is 14.14 births per 1,000 population.
- 19. Average life expectancy for females is 80 years.
- 20. Approximately 290 million persons residing in the United States. Each of these numbers is referred to as statistic. True/False Answer: True Prof. Ahmed Hassen
- 21. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Questions of Chapter 1 7 . The main purpose of descriptive statistics is to:Summarize data in a useful and informative manner. Make inferences about a population. Determine if the data adequately represents The Population.Gather Or Collect DataAnswer: A Prof. Ahmed Hassen Prof. Ahmed Hassen
- 22. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability Questions of Chapter 1 9 . Respondents were asked, " Do you now earnmore than or less than you did five years ago?" What is this level of measurement? Interval Ratio Nominal OrdinalNone of the above Answer: D Prof. Ahmed Hassen
- 23. MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability MST 103 – Introduction to Statistics and Probability End of Chapter 1 Prof. Ahmed Hassen