1 / 9

Chapter 1: Exploring Data Review

Chapter 1: Exploring Data Review. Vaishali Saseenthar Christine Kil Aditi Bellur. The BIG Idea. Data Production Data Analysis Probability Statistical Inference Presenting the statistics: Plotting the data: Dotplot , stemplot , histogram.

sigourney-buckley
Download Presentation

Chapter 1: Exploring Data Review

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Chapter 1:Exploring DataReview VaishaliSaseenthar Christine Kil AditiBellur

  2. The BIG Idea • Data Production • Data Analysis • Probability • Statistical Inference • Presenting the statistics: • Plotting the data: Dotplot, stemplot, histogram. • Interpreting the data: SOCS (shape, outliers, center, spread) • Numerical summary: mean and SD, five-number summary

  3. When do YOU use this?? • We use graphs such as histograms, stemplots, pie graphs, and bar graphs to organize and present the data. • Data analysis is used to draw conclusions from our data. • Examining the distributions for shape, center, spread, and deviations helps understandthe data. • Data analysis is used practically in economics, politics, journalism, and other fields.

  4. Vocab to know • Statistical inference- drawing conclusions about a large group based on a smaller group. • Surveys- popular ways to gauge public opinion; asks the individuals in the sample some questions and record their responses. • Observational study- observing individuals and measuring variables but no influencing the responses. • Experiment- deliberately influencing individuals to observe responses. • Individuals- objects described by the data. • Variable- any characteristic of the individual. • Categorical variable- individual is in one of several groups. • Quantitative variable- numerical values for adding and averaging. • Mean- average value x-bar. Is sensitive to the influence of a few extreme observation. • Outlier- individual value that falls outside the overall pattern.

  5. Vocab to know dos • pth percentile of a distribution is the value such that p% of the observations fall at or below it. • First quartile Q₁- median of observations to the left of the overall median in the ordered list. • Third quartile Q₃- median of observations to the right of the overall median in the ordered list. • Five-number summary of a set of observations = the smallest observation, the first quartile, the median, the third quartile, and the largest observation written smallest to largest. • Boxplot- graph of the five-number summary. • Interquartile range- the distance between the quartiles (the range of the center half of the data) is a more resistant 50% of range. • Standard deviation measure spread by looking at how far the observations are from their mean. • Variance of a set of observations- the average of the squares of the deviations of the observations from their mean. • Data analysis- art of describing data using graphs and numerical summaries.

  6. Key Topics Covered in this Chapter • Graphs for Categorical Variables • Stem plots • Histograms • Examining Distributions • Dealing with Outliers • Relative Frequency and Cumulative Frequency • Time Plots • Measuring Center: The Mean • Mean versus Median • Measuring spread: The Quartiles • The Five-Number Summary and Boxplots • The 1.5 X IQR Rule for Suspected Outliers • Measuring Spread: The Standard deviation • Properties of Standard Deviation • Choosing Measures of Center and Spread • Changing the Unit of Measurement • Comparing Distributions

  7. Calculator Key Strokes • Calculator Boxplots and Numerical Summaries • Enter first set of data in L1/ list 1 and second set in L2/ list 2 • Set up two statistics plots: Plot 1 to show modified boxplot of data in list 1 and Plot 2 to show modified boxplot of data in list 2 • Use the calculators zoom feature to display the side-by-side boxplots • Calculate numerical summaries for each set of data • Notice the down-arrow on the left side of the display. Press down to see other statistics.

  8. Formulas You Should Know • Mean: x-bar = x(1) + x(2)….+ x(n) • Variance: • Standard Deviation: • Linear transformation: X(new)= a + bx n

  9. Helpful HINTS • Stemplots do not work well for large data sets where each stem must hold a large # of leaves. • Use histograms of %’s for comparing several distributions with different # of observations. • When examining a distribution, look for the overall pattern and striking deviations from the pattern. • Look for outliers that are clearly apart from the body of the data, not just in the most extreme observations. • The simplest useful numerical description of a distribution is the measure of center and measure of spread.

More Related