U can statistics for dummies / by Deborah Rumsey with David Unger.

Cover Title : Step-by-step lessons and practice for statistics

Spine Title : U can step-by-step lessons and practice for Statistics

"A Wiley brand."

Includes index.

Introduction: -- About this book -- Foolish assumptions -- Beyond the book -- Where to go from here -- Part 1: Getting Started With Statistics: -- Statistics Of Everyday Life: -- Statistics and the media: more questions than answers?: -- Probing popcorn problems -- Venturing into viruses -- Comprehending crashes -- Mulling malpractice -- Belaboring the loss of land -- Scrutinizing schools -- Study sports -- Banking on business news -- Touring the travel news -- Surveying sexual stats -- Breaking down weather reports -- Using statistics at work: -- Delivering babies-and information -- Posing for pictures -- Poking through pizza data -- Statistics in the office -- Taking Control: So Many Numbers, So Little Time: -- Detecting errors, exaggerations, and just plain lies: -- Checking the math -- Uncovering misleading statistics -- Looking for lies in all the right places -- Feeling the impact of misleading statistics -- Tools Of The Trade: -- Thriving in a statistical world -- Statistics: more than just numbers -- Designing appropriate studies: -- Surveys (polls) -- Experiments -- Collecting quality data: -- Sample, random, or otherwise -- Bias -- Grabbing some basic statistical jargon: -- Data -- Data set -- Variable -- Population -- Statistic -- Parameter -- Mean (average) -- Median -- Standard deviation -- Percentile -- Standard score -- Distribution and normal distribution -- Central limit theorem -- z-values -- Margin of error -- Confidence interval -- Hypothesis testing -- p-values -- Statistical significance -- Correlation, regression, and two-way tables -- Drawing credible conclusions: -- Reeling in overstated results -- Questioning claims of cause and effect -- Becoming a sleuth, not a skeptic -- Part 2: Number-Crunching Basics: -- Crunching Categorical Data: -- Summing up data with descriptive statistics -- Crunching categorical data: tables and percent's: -- Counting on the frequency -- Relating with percentages -- Two-way tables: summarizing multiple measures -- Interpreting counts and percent's with caution -- Means, Medians, And More: -- Measuring the center with mean and median: -- Averaging out to the mean -- Splitting your data down the median -- Comparing means and medians: histograms -- Accounting for variation: -- Reporting the standard deviation -- Being out of range -- Examining the empirical rule (68-95-99-7) -- Measuring relative standing with percentiles: -- Calculating percentiles -- Interpreting percentiles -- Gathering a five-number summary -- Exploring interquartile range -- Getting The Picture: Graphing Categorical Data: -- Take another little piece of my pie chart: -- Tallying personal expenses -- Bringing in a lotto revenue -- Ordering takeout -- Projecting age trends -- Raising the bar on bar graphs: -- Tracking transportation expenses -- Making a lotto profit -- Tipping the scales on a bar graph -- Pondering pet peeves -- Going By The Numbers: Graphing Numerical Data: -- Handling histograms: -- Making a histogram -- Interpreting a histogram -- Putting numbers with pictures -- Detecting misleading histograms -- Examining boxplots: -- Making a boxplot -- Interpreting a boxplot -- Tackling time charts: -- Interpreting time charts -- Understanding variability: time charts versus histograms -- Spotting misleading time charts -- Part 3: Distributions And The Central Limit Theorem: -- Coming To Terms With Probability: -- Set notation overview: -- Noting outcomes: sample spaces -- Noting subsets of sample spaces: events -- Noting a void in the set: empty sets -- Putting sets together: unions, intersections, and complements -- Probabilities of events involving A and/or B: -- Probability notation -- Marginal probabilities -- Union probabilities -- Intersection (joint) probabilities -- Complement probabilities -- Conditional probabilities -- Understanding and applying the rules of probability: -- Complement rule (for opposites, not for flattering a date) -- Multiplication rule (for intersections, not for rabbits) -- Addition rule (for unions of the nonmarital nature) -- Recognizing independence in multiple events: -- Checking independence for two events with the definition -- Using the multiplication rule for independent events -- Including mutually exclusive events: -- Recognizing mutually exclusive events: -- Simplifying the addition rule with mutually exclusive events -- Distinguishing independent from mutually exclusive events: -- Comparing and contrasting independence and exclusivity -- Checking for independence or exclusivity in a 52-card deck -- Avoiding probability misconceptions -- Predictions using probability -- Random Variables And The Binomial Distribution: -- Defining a random variable: -- Discrete versus continuous -- Probability distributions -- Mean and variance of a discrete random variable -- Identifying a binomial: -- Checking binomial conditions step by step -- No fixed number of trials -- More than success or failure -- Trials are not independent -- Probability of success (p) changes -- Finding binomial probabilities using a formula -- Finding probabilities using the binomial table: -- Finding probabilities for specific values of x -- Finding probabilities for x greater-than, less-than, or between two values -- Checking out the mean and standard deviation of the binomial -- Normal Distribution: -- Exploring the basics of the normal distribution -- Meeting the standard normal (Z-) distribution: -- Checking out Z -- Standardizing from X to Z -- Finding probabilities for Z with the Z-table -- Finding probabilities for a normal distribution -- Knowing where you stand with percentiles -- Finding X when you know the percent: -- Figuring out a percentile for a normal distribution -- Translating tricky wording in percentile problems -- Normal approximation to the binomial -- t-Distribution: -- Basics of the t-distribution: -- Comparing the t- and Z-distributions -- Discovering the effect of variability on t-distributions -- Using the t-table: -- Finding probabilities with the t-table -- Figuring percentiles for the t-distribution -- Picking out t*-values for confidence intervals -- Studying behavior using the t-table -- Sampling Distributions And the Central Limit Theorem: -- Defining a sampling distribution -- Mean of a sampling distribution -- Measuring standard error: -- Sample size and standard error -- Population standard deviation and standard error -- Looking at the shape of a sampling distribution: -- Case 1: Distribution of X is normal -- Case 2: Distribution of X is not normal-enter the central limit theorem -- Finding probabilities for the sample mean -- Sampling distribution of the sample proportion -- Finding probabilities for the sample proportion --

Part 4: Guesstimating And Hypothesizing With Confidence: -- Leaving Room For A Margin Of Error: -- Seeing the importance of that plus or minus -- Finding the margin of error: a general formula: -- Measuring sample variability -- Calculating margin of error for a sample proportion -- Reporting results -- Calculating margin of error for a sample mean -- Being confident you're right -- Determining the impact of sample size: -- Sample size and margin of error -- Bigger isn't always (that much) better! -- Keeping margin of error in perspective -- Confidence Intervals: Making Your Best Guesstimate: -- Not all estimates are created equal -- Linking a statistic to a parameter -- Getting with the jargon -- Interpreting results with confidence -- Zooming in on width -- Choosing a confidence level -- Factoring in the sample size -- Counting on population variability -- Calculating a confidence interval for a population mean: -- Case 1: Population standard deviation is known -- Case 2: Population standard deviation is unknown and/or n is small -- Figuring out what sample size you need -- Determining the confidence interval for one population proportion -- Creating a confidence interval for the difference of two means: -- Case 1: Population standard deviations are known -- Case 2: Population standard deviations are unknown and/or sample sizes are small -- Estimating the difference of two proportions -- Spotting misleading confidence intervals -- Claims, Tests, And Conclusions: -- Setting up the hypotheses: -- Defining the null -- What's the alternative? -- Gathering good evidence (data) -- Compiling the evidence: the test statistic: -- Gathering sample statistics -- Measuring variability using standard errors -- Understanding standard scores -- Calculating and interpreting the test statistic -- Weighing the evidence and making decisions: p-values: -- Connecting test statistics and p-values -- Defining a p-value -- Calculating a p-value -- Making conclusions: -- Setting boundaries for rejecting H -- Testing varicose veins -- Assessing the chance of a wrong decision: -- Making a false alarm: type I errors -- Missing out on a detection: type II errors -- Commonly Used Hypothesis Tests: Formulas And Examples: -- Testing one population mean -- Handling small samples and unknown standard deviations: the t-test: -- Putting the t-test to work -- Relating t to Z -- Handling negative t-values -- Examining the not-equal-to alternative -- Drawing conclusions using the critical value -- Testing one population proportion -- Comparing two (independent) population averages -- Testing for an average difference (the paired t-test) -- Comparing two population proportions -- Part 5: Statistical Studies And The Hunt For A Meaningful Relationship: -- Polls, Polls, And More Polls: -- Recognizing the impact of polls: -- Getting to the source -- Surveying what's hot -- Impacting lives -- Behind the scenes: the ins and outs of surveys: -- Planning and designing a survey -- Selecting the sample -- Carrying out a survey -- Interpreting results and finding problems -- Experiments: Medical Breakthroughs Or Misleading Results?: -- Boiling down the basics of studies: -- Looking at the lingo of studies -- Observing observational studies -- Examining experiments -- Designing a good experiment: -- Designing the experiment to make comparisons -- Selecting the sample size -- Choosing the subjects -- Making random assignments -- Controlling for confounding variables -- Respecting ethical issues -- Collecting good data -- Analyzing the data properly -- Interpreting experiment results: -- Making appropriate conclusions -- Making informed decisions -- Looking For Links: Correlation And Regression: -- Picturing a relationship with a scatterplot: -- Making a scatterplot -- Interpreting a scatterplot -- Quantifying linear relationships using the correlation: -- Calculating the correlation -- Interpreting the correlation -- Examining properties of the correlation -- Working with linear regression: -- Figuring out which variable is X and which is Y -- Checking the conditions -- Calculating the regression line -- Interpreting the regression line -- Putting it all together with an example: the regression line for the crickets -- Making proper predictions: -- Checking the conditions -- Staying in-bounds -- Explaining the relationship: correlation versus cause and effort -- Two-Way Tables And Independence: -- Organizing a two-way table: -- Setting up the cells -- Figuring the totals -- Interpreting two-way tables: -- Singling out variables with marginal distributions -- Examining all groups-a joint distribution -- Comparing groups with conditional distributions -- Checking independence and describing dependence: -- Checking for independence -- Describing a dependent relationship -- Cautiously interpreting results: -- Checking for legitimate cause and effect -- Projecting from sample to population -- Making prudent predictions -- Resisting the urge to jump to conclusions -- Part 6: Part Of Tens: -- Ten Tips For The Statistically Savvy Sleuth: -- Pinpoint misleading graphs: -- Pie charts -- Bar graphs -- Time charts -- Histograms -- Uncover biased data -- Search for a margin of error -- Identify nonrandom samples -- Sniff out missing sample sizes -- Detect misinterpreted correlations -- Reveal confounding variables -- Inspect the numbers -- Report selective reporting -- Expose the anecdote -- Ten Surefire Exam Score Boosters: -- Know what you don't know, and then do something about it -- Avoid "yeah-yeah" traps: -- Yeah-Yeah Trap #1 -- Yeah-Yeah Trap #2 -- Make friends with formulas -- Make an "if-then-how" chart -- Figure out what the question is asking -- Label what you're given -- Draw a picture -- Make the connection and solve the problem -- Do the math-twice -- Analyze your answers -- Appendix: -- Z-table -- T-table -- Binomial table -- Index

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