U can : Statistics for dummies / by Deborah J. Rumsey, with David Unger.
- 2 of 2 copies available at Evergreen Indiana.
0 current holds with 2 total copies.
|Location||Call Number / Copy Notes||Barcode||Shelving Location||Status||Due Date|
|Jackson Co PL - Seymour Main Library||519.5 RUMSEY (Text)||37500004223653||New Items||Available||-|
|Jefferson Co PL - Hanover Branch||519.5 RUM (Text)||39391100230307||Non-Fiction||Available||-|
- ISBN: 9781119084853
- ISBN: 1119084857
- Physical Description: xiii, 511 pages : illustrations, charts ; 26 cm.
- Publisher: Hoboken, New Jersey : John Wiley & Sons, Inc., 
- Copyright: ©2015
|General Note:|| "A Wiley brand."
|Formatted Contents Note:|| 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
|Summary, etc.:|| Make studying statistics simple with this easy-to-read resource. Wouldn't it be wonderful if studying statistics were easier? With U Can: Statistics I For Dummies, it is! This one-stop resource combines lessons, practical examples, study questions, and online practice problems to provide you with the ultimate guide to help you score higher in your statistics course. Foundational statistics skills are a must for students of many disciplines, and leveraging study materials such as this one to supplement your statistics course can be a life-saver. Because U Can: Statistics I For Dummies contains both the lessons you need to learn and the practice problems you need to put the concepts into action, you'll breeze through your scheduled study time. Statistics is all about collecting and interpreting data, and is applicable in a wide range of subject areas which translates into its popularity among students studying in diverse programs. So, if you feel a bit unsure in class, rest assured that there is an easy way to help you grasp the nuances of statistics!. -- Understand statistical ideas, techniques, formulas, and calculations. -- Interpret and critique graphs and charts, determine probability, and work with confidence intervals. -- Critique and analyze data from polls and experiments. -- Combine learning and applying your new knowledge with practical examples, practice problems, and expanded online resources U Can: Statistics I For Dummies contains everything you need to score higher in your fundamental statistics course!.
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