Statistics for people who (think they) hate statistics /
Salkind, Neil J.
Statistics for people who (think they) hate statistics / Neil J. Salkind. - Excel ed. - Thousand Oaks : SAGE Publications, c2007. - xviii, 403 p. : ill. ; 26 cm.
This edition shows the students how to install the Excel Analysis ToolPak option (free) to earn access to a host of new and very useful analytical techniques.
Includes bibliographical references and index.
pt. I. Yippee! I'm in statistics -- 1. Statistics or sadistics? : it's up to you -- Why statistics? -- And why Excel? -- A five-minute history of statistics -- Statistics : what it is (and isn't) -- What are descriptive statistics? -- What are inferential statistics? -- In other words... -- Tooling around with the Analysis ToolPak -- What am I doing in a statistics class? -- Ten way to use this book (and learn statistics at the same time!) -- About those icons -- Key to difficulty icons-- Key to "how much Excel" icons -- Glossary -- Summary -- Time to practice -- 1a. All you need to know about formulas and functions -- What's a formula? -- Creating a formula -- Operator, operator, get me a formula! -- Beware the parentheses -- What's a function? -- Using a function -- Using functions in formulas -- We're taking names : naming ranges -- Using ranges -- Summary -- Time to practice -- Answers to practice questions -- 1b. All you need to know about using the amazing data Analysis ToolPak -- A look at a data analysis tool -- Don't have it? -- pt. II. Sigma Freud and descriptive statistics -- 2. Computing and understanding averages : means to an end -- Computing the mean -- And now...using Excel's AVERAGE function -- Things to remember -- Computing a weighted mean -- Computing the median -- And now...using Excel's MEDIAN function -- Things to remember -- Computing the mode -- And now...using Excel's MODE function -- Apple pie áa la biomodal -- Using the amazing Analysis ToolPak to compute descriptive statistics -- Make the Analysis ToolPak output pretty -- When to use what -- Summary -- Time to practice -- Answers to practice questions -- 3. Vive la diffâerence : understanding variability -- Why understanding variability is important -- Computing the range -- Computing the standard deviation -- And now...using Excel's STDEV function -- Why n - 1? : what's wrong with just n? -- What's the big deal? -- Things to remember -- Computing the variance -- And now...using Excel's VAR function -- The standard deviation versus the variance -- Using the amazing Analysis ToolPak (Again!) -- Summary -- Time to practice -- Answer to practice questions -- 4. A picture really is worth a thousand words -- Why illustrate data? -- Ten ways to a great figure (eat less and exercise more?) -- First things first : creating a frequency distribution -- The classiest of intervals -- The plot thickens : creating a histogram -- The tally-ho method -- Using the amazing Analysis ToolPak to create a histogram -- The next step : a frequency polygon -- Cumulating frequencies -- Fat and skinny frequency distributions -- Average value -- Variability -- Skewness -- Kurtosis -- Excellent charts -- Your first Excel chart : a moment to remember -- Excellent charts part deux : making charts pretty -- Other cool charts -- Bar charts -- Line charts -- Pie charts -- Summary -- Time to practice -- Answer to practice questions -- 5. Ice cream and crime : computing correlation coefficients -- What are correlations all about? -- Types of correlation -- Coefficients : flavor 1 and flavor 2 -- Things to remember -- Computing a simple correlation coefficient -- And now...using Excel's CORREL function -- A visual picture of a correlation : the scatterplot -- Using Excel to create a scatterplot -- Bunches of correlations : the correlation matrix -- More Excel, bunches of correlations áa la Excel -- Using the amazing Analysis ToolPak to computer correlations -- Understanding what the correlation coefficient means -- Using-your-thumb rule -- A determined effort : squaring the correlation coefficient -- As more ice cream is eaten...the crime rate goes up (or association versus causality) -- Other cool correlations -- Summary -- Time to practice -- Answers to practice questions -- pt. III. Taking chances for fun and profit -- 6. Hypotheticals and you : testing your questions -- So you want to be a scientist... -- Samples and populations -- The null hypothesis -- The purpose of the null hypothesis -- The research hypothesis -- The nondirectional research hypothesis -- The directional research hypothesis -- Some differences between the null hypothesis and the research hypothesis -- What makes a good hypothesis? -- Summary -- Time to practice -- Answer to practice questions -- 7. Are your curves normal? : probability and why it counts -- Why probability? -- The normal curve (a.k.a. the bell-shaped curve) -- Hey, that's not normal! -- More normal curve 101 -- Our favorite standard score : the z score -- Using Excel to computer z scores -- What z scores represent -- What z scores really represent -- Hypothesis testing and z scores : the first step -- Summary -- Time to practice -- Answers to practice questions -- pt. IV. Significantly different : using inferential statistics -- 8. Significantly significant : what it means for you and me -- The concept of significance -- If only we were perfect -- The world's most important table (for this semester only) -- More about table 8.1 -- Back to type I errors -- Significance versus meaningfulness -- An introduction to inferential statistics -- How inference works -- How to select what test to use -- Here's how to use the chart -- An introduction to tests of significance -- How a test of significance works : the plan -- Here's the picture that's worth a thousand words -- Summary -- Time to practice -- Answers to practice questions -- 9. t(ea) for two : tests between the means of different groups -- Introduction to the t test for independent samples -- The path to wisdom and knowledge -- Computing the test statistic -- So how do I interpret t(58) = -.14, p > .05? -- And now...using Excel's TTEST function -- Using the amazing data Analysis ToolPak to compute the t value -- Special effects : are those differences for real? -- Computing and understanding the effect size -- A very cool effect size calculator -- Summary -- Time to practice -- Answers to practice questions -- 10. t(ea) for two (again) : tests between the means of related groups -- Introduction to the t test for dependent samples -- The path to wisdom and knowledge -- Computing the test statistic -- So how do I interpret t(24) = 2.45, p < .05? -- And now...using Excel's TTEST function -- Using the amazing data Analysis ToolPak to computer the t value -- Summary -- Time to practice -- Answers to practice questions -- 11. Two groups too many? : try analysis of variance -- Introduction to analysis of variance -- The path to wisdom and knowledge -- Different flavors of ANOVA -- Computing the F test statistic -- So how do I interpret F(2, 27) = 8.80, p < .05? -- And now...using Excel's FDIST and FTEST functions -- Using the amazing data Analysis ToolPak to compute the F value -- Summary -- Time to practice -- Answers to practice questions -- 12. Two too many factors : factorial analysis of variance : a brief introduction -- Introduction to factorial analysis of variance -- Two flavors of factorial ANOVA -- The path to wisdom and knowledge -- A new flavor of ANOVA -- The main event : main effects in factorial ANOVA -- Even more interesting : interaction effects -- Computing the ANOVA F statistic -- Using the amazing data Analysis ToolPak -- Summary -- Time to practice -- Answers to practice questions -- 13. Cousins or just good friends? : testing relationships using the correlation coefficient -- Introduction to testing the correlation coefficient -- The path to wisdom and knowledge -- Computing the test statistic -- So how do I interpret 7(27) = .393, p < .05? -- Causes and associations (again!) -- Significance versus meaningfulness (again, again!) -- Summary -- Time to practice -- Answers to practice questions -- 14. Predicting who'll win the Super Bowl : using linear regression -- What is prediction all about? -- The logic of prediction -- Drawing the world's best line (for your data) -- And now...using Excel's SLOPE function -- And now...using Excel's INTERCEPT function -- How good is our prediction? -- The more predictors the better? : maybe -- The big rule when it comes to using multiple predictor variables -- Summary -- Time to practice -- Answers to practice questions -- 15. What to do when you're not normal : chi-square and some other nonparametric tests -- Introduction to nonparametric statistics -- Introduction to one-sample chi-square -- Computing the chi-square test statistic -- So how do I interpret X�p2�s(2) = 20.6, p < .05? -- And now...using Excel's CHIDIST function -- Other nonparametric tests you should know about -- Summary -- Time to practice -- Answers to practice questions -- 16. Just the truth : an introduction to understanding reliability and validity -- An introduction to reliability and validity -- What's up with this measurement stuff? -- All about measurement scales -- A rose by any other name : the nominal level of measurement -- Any order is fine with me : the ordeal level of measurement -- 1 + 1 = 2 : the interval level of measurement -- Can anyone have nothing of anything? : the ratio level of measurement -- In sum... -- Reliability, doing it again until you get it right -- Test scores, truth or dare -- Observed score = true score + error score -- Different types of reliability -- How big is big? -- Interpreting reliability coefficients -- Just one more thing -- Validity, whoa! : what is the truth? -- Different types of validity -- A last, friendly word -- Validity and reliability : really close cousins -- Time to practice -- Answers to practice questions -- 17. Some other (important) statistical procedures you should know about -- Multivariate analysis of variance -- Repeated measures analysis of variance -- Analysis of covariance -- Multiple regression -- Factor analysis -- Path analysis -- Structural equation modeling -- Summary -- 18. A statistical software sampler -- Selecting the perfect statistics software -- What's out there -- The free stuff -- Time to pay -- Summary -- pt. V. Ten things you'll want to know and remember -- 19. The ten (or more) best Internet sites for statistics stuff -- Tons and tons of resources -- Calculators galore! -- Who's who and what's happened -- It's all here -- HyperStat -- Data? : you want data? -- More and more and more and more resources -- Plain, but fun -- How about studying statistics in Stockholm? -- Online statistical teaching materials -- More and more and more stuff -- 20. The ten commandments of data collection -- Appendix A. Excel-erate your learning : all you need to know about Excel -- Appendix B. Tables -- Appendix C. The data sets -- Glossary.
1412924812 (cloth : acid-free paper) 9781412924818 (cloth : acid-free paper) 1412924820 (pbk. : acid-free paper) 9781412924825
2006004930
Microsoft Excel (Computer file)
Statistics.
Statistiek.
Microsoft Excel.
HA29 / .S164s 2007
519.5
Statistics for people who (think they) hate statistics / Neil J. Salkind. - Excel ed. - Thousand Oaks : SAGE Publications, c2007. - xviii, 403 p. : ill. ; 26 cm.
This edition shows the students how to install the Excel Analysis ToolPak option (free) to earn access to a host of new and very useful analytical techniques.
Includes bibliographical references and index.
pt. I. Yippee! I'm in statistics -- 1. Statistics or sadistics? : it's up to you -- Why statistics? -- And why Excel? -- A five-minute history of statistics -- Statistics : what it is (and isn't) -- What are descriptive statistics? -- What are inferential statistics? -- In other words... -- Tooling around with the Analysis ToolPak -- What am I doing in a statistics class? -- Ten way to use this book (and learn statistics at the same time!) -- About those icons -- Key to difficulty icons-- Key to "how much Excel" icons -- Glossary -- Summary -- Time to practice -- 1a. All you need to know about formulas and functions -- What's a formula? -- Creating a formula -- Operator, operator, get me a formula! -- Beware the parentheses -- What's a function? -- Using a function -- Using functions in formulas -- We're taking names : naming ranges -- Using ranges -- Summary -- Time to practice -- Answers to practice questions -- 1b. All you need to know about using the amazing data Analysis ToolPak -- A look at a data analysis tool -- Don't have it? -- pt. II. Sigma Freud and descriptive statistics -- 2. Computing and understanding averages : means to an end -- Computing the mean -- And now...using Excel's AVERAGE function -- Things to remember -- Computing a weighted mean -- Computing the median -- And now...using Excel's MEDIAN function -- Things to remember -- Computing the mode -- And now...using Excel's MODE function -- Apple pie áa la biomodal -- Using the amazing Analysis ToolPak to compute descriptive statistics -- Make the Analysis ToolPak output pretty -- When to use what -- Summary -- Time to practice -- Answers to practice questions -- 3. Vive la diffâerence : understanding variability -- Why understanding variability is important -- Computing the range -- Computing the standard deviation -- And now...using Excel's STDEV function -- Why n - 1? : what's wrong with just n? -- What's the big deal? -- Things to remember -- Computing the variance -- And now...using Excel's VAR function -- The standard deviation versus the variance -- Using the amazing Analysis ToolPak (Again!) -- Summary -- Time to practice -- Answer to practice questions -- 4. A picture really is worth a thousand words -- Why illustrate data? -- Ten ways to a great figure (eat less and exercise more?) -- First things first : creating a frequency distribution -- The classiest of intervals -- The plot thickens : creating a histogram -- The tally-ho method -- Using the amazing Analysis ToolPak to create a histogram -- The next step : a frequency polygon -- Cumulating frequencies -- Fat and skinny frequency distributions -- Average value -- Variability -- Skewness -- Kurtosis -- Excellent charts -- Your first Excel chart : a moment to remember -- Excellent charts part deux : making charts pretty -- Other cool charts -- Bar charts -- Line charts -- Pie charts -- Summary -- Time to practice -- Answer to practice questions -- 5. Ice cream and crime : computing correlation coefficients -- What are correlations all about? -- Types of correlation -- Coefficients : flavor 1 and flavor 2 -- Things to remember -- Computing a simple correlation coefficient -- And now...using Excel's CORREL function -- A visual picture of a correlation : the scatterplot -- Using Excel to create a scatterplot -- Bunches of correlations : the correlation matrix -- More Excel, bunches of correlations áa la Excel -- Using the amazing Analysis ToolPak to computer correlations -- Understanding what the correlation coefficient means -- Using-your-thumb rule -- A determined effort : squaring the correlation coefficient -- As more ice cream is eaten...the crime rate goes up (or association versus causality) -- Other cool correlations -- Summary -- Time to practice -- Answers to practice questions -- pt. III. Taking chances for fun and profit -- 6. Hypotheticals and you : testing your questions -- So you want to be a scientist... -- Samples and populations -- The null hypothesis -- The purpose of the null hypothesis -- The research hypothesis -- The nondirectional research hypothesis -- The directional research hypothesis -- Some differences between the null hypothesis and the research hypothesis -- What makes a good hypothesis? -- Summary -- Time to practice -- Answer to practice questions -- 7. Are your curves normal? : probability and why it counts -- Why probability? -- The normal curve (a.k.a. the bell-shaped curve) -- Hey, that's not normal! -- More normal curve 101 -- Our favorite standard score : the z score -- Using Excel to computer z scores -- What z scores represent -- What z scores really represent -- Hypothesis testing and z scores : the first step -- Summary -- Time to practice -- Answers to practice questions -- pt. IV. Significantly different : using inferential statistics -- 8. Significantly significant : what it means for you and me -- The concept of significance -- If only we were perfect -- The world's most important table (for this semester only) -- More about table 8.1 -- Back to type I errors -- Significance versus meaningfulness -- An introduction to inferential statistics -- How inference works -- How to select what test to use -- Here's how to use the chart -- An introduction to tests of significance -- How a test of significance works : the plan -- Here's the picture that's worth a thousand words -- Summary -- Time to practice -- Answers to practice questions -- 9. t(ea) for two : tests between the means of different groups -- Introduction to the t test for independent samples -- The path to wisdom and knowledge -- Computing the test statistic -- So how do I interpret t(58) = -.14, p > .05? -- And now...using Excel's TTEST function -- Using the amazing data Analysis ToolPak to compute the t value -- Special effects : are those differences for real? -- Computing and understanding the effect size -- A very cool effect size calculator -- Summary -- Time to practice -- Answers to practice questions -- 10. t(ea) for two (again) : tests between the means of related groups -- Introduction to the t test for dependent samples -- The path to wisdom and knowledge -- Computing the test statistic -- So how do I interpret t(24) = 2.45, p < .05? -- And now...using Excel's TTEST function -- Using the amazing data Analysis ToolPak to computer the t value -- Summary -- Time to practice -- Answers to practice questions -- 11. Two groups too many? : try analysis of variance -- Introduction to analysis of variance -- The path to wisdom and knowledge -- Different flavors of ANOVA -- Computing the F test statistic -- So how do I interpret F(2, 27) = 8.80, p < .05? -- And now...using Excel's FDIST and FTEST functions -- Using the amazing data Analysis ToolPak to compute the F value -- Summary -- Time to practice -- Answers to practice questions -- 12. Two too many factors : factorial analysis of variance : a brief introduction -- Introduction to factorial analysis of variance -- Two flavors of factorial ANOVA -- The path to wisdom and knowledge -- A new flavor of ANOVA -- The main event : main effects in factorial ANOVA -- Even more interesting : interaction effects -- Computing the ANOVA F statistic -- Using the amazing data Analysis ToolPak -- Summary -- Time to practice -- Answers to practice questions -- 13. Cousins or just good friends? : testing relationships using the correlation coefficient -- Introduction to testing the correlation coefficient -- The path to wisdom and knowledge -- Computing the test statistic -- So how do I interpret 7(27) = .393, p < .05? -- Causes and associations (again!) -- Significance versus meaningfulness (again, again!) -- Summary -- Time to practice -- Answers to practice questions -- 14. Predicting who'll win the Super Bowl : using linear regression -- What is prediction all about? -- The logic of prediction -- Drawing the world's best line (for your data) -- And now...using Excel's SLOPE function -- And now...using Excel's INTERCEPT function -- How good is our prediction? -- The more predictors the better? : maybe -- The big rule when it comes to using multiple predictor variables -- Summary -- Time to practice -- Answers to practice questions -- 15. What to do when you're not normal : chi-square and some other nonparametric tests -- Introduction to nonparametric statistics -- Introduction to one-sample chi-square -- Computing the chi-square test statistic -- So how do I interpret X�p2�s(2) = 20.6, p < .05? -- And now...using Excel's CHIDIST function -- Other nonparametric tests you should know about -- Summary -- Time to practice -- Answers to practice questions -- 16. Just the truth : an introduction to understanding reliability and validity -- An introduction to reliability and validity -- What's up with this measurement stuff? -- All about measurement scales -- A rose by any other name : the nominal level of measurement -- Any order is fine with me : the ordeal level of measurement -- 1 + 1 = 2 : the interval level of measurement -- Can anyone have nothing of anything? : the ratio level of measurement -- In sum... -- Reliability, doing it again until you get it right -- Test scores, truth or dare -- Observed score = true score + error score -- Different types of reliability -- How big is big? -- Interpreting reliability coefficients -- Just one more thing -- Validity, whoa! : what is the truth? -- Different types of validity -- A last, friendly word -- Validity and reliability : really close cousins -- Time to practice -- Answers to practice questions -- 17. Some other (important) statistical procedures you should know about -- Multivariate analysis of variance -- Repeated measures analysis of variance -- Analysis of covariance -- Multiple regression -- Factor analysis -- Path analysis -- Structural equation modeling -- Summary -- 18. A statistical software sampler -- Selecting the perfect statistics software -- What's out there -- The free stuff -- Time to pay -- Summary -- pt. V. Ten things you'll want to know and remember -- 19. The ten (or more) best Internet sites for statistics stuff -- Tons and tons of resources -- Calculators galore! -- Who's who and what's happened -- It's all here -- HyperStat -- Data? : you want data? -- More and more and more and more resources -- Plain, but fun -- How about studying statistics in Stockholm? -- Online statistical teaching materials -- More and more and more stuff -- 20. The ten commandments of data collection -- Appendix A. Excel-erate your learning : all you need to know about Excel -- Appendix B. Tables -- Appendix C. The data sets -- Glossary.
1412924812 (cloth : acid-free paper) 9781412924818 (cloth : acid-free paper) 1412924820 (pbk. : acid-free paper) 9781412924825
2006004930
Microsoft Excel (Computer file)
Statistics.
Statistiek.
Microsoft Excel.
HA29 / .S164s 2007
519.5