1. Midterm Review IN CLASS

2. Chapter 1: The Art and Science of Data 1.Recognize individuals and variables in a statistical study. 2.Distinguish between categorical and quantitative variables. 3.Identify the population and the sample in a statistical study. 4.Distinguish observational studies from experiments.

3. Chapter 2: Describing Distributions of Data 1.Recognize when a pie chart can and cannot be used. 2.Make a bar graph of the distribution of a categorical variable or, in general, to compare related quantities. 3.Interpret pie charts and bar graphs. 4.Be aware of graphical abuses especially in line graphs and pictograms. 5.Make a dotplot of the distribution of a small set of observations. 6.Make a histogram, using the calculator, of the distribution of a quantitative variable.

4. Chapter 2: Describing Distributions of Data 7. Describe a dotplot, stemplot, or histogram using SOCS! (Shape, Outliers, Center, Spread). 8.Describe the overall pattern by giving numerical measures of spread and center. 9.Decide which measures of center and spread are more appropriate: The mean and standard deviation or the median and IQR. 10.Identify outliers using the 1.5*IQR rule. 11.Compare distributions of categorical or quantitative variables.

5. Chapter 2: Describing Distributions of Data 12. Find the mean, standard deviation, quartiles, medial, IQR on the calculator using 1-VarStats on the calculator. 13. Give the five-number summary. Create a modified boxplot on the calculator. Sketch it. Use side-by-side boxplots to compare two distributions. 14. Understand that the median is less affected by extreme observations than the mean. Recognize that the skewness in a distribution moves the mean away from the median toward the long tail. 15. Know the basic properties of the standard deviation: s = 0 only when all observations are identical; s increases as spread increases; s has the same units as the original measurements; s is pulled strongly up by outliers or skewness.

6. Chapter 3: Modeling Distributions of Data 1.Calculate and use percentiles to locate individual values within a distribution of data. 2.Find the standardized value (z-score) of an observation. Interpret z-scores in context. 3.Know that areas under a density curve represent proportion of all observations and that the total area under a density curve is 1.

7. Chapter 3: Modeling Distribution of Data 4. Recognize the shape of Normal curves. 5. Use the 68-95-99.7 rule, Table A, or the calculator to find the percent of observations from a Normal distribution. 6. Use Table A, or the calculator to find the percentile value from any Normal distribution and the value that corresponds to a given percentile.

8. Chapter 4: Describing Relationships 1.Make a scatterplot to display the relationship between two quantitative variables measured on the same subjects. Place the explanatory variable on the horizontal axis. 2.Describe the direction, form, and strength of the overall pattern of the scatterplot. In particular, recognize positive or negative associations and straight-line patterns. Recognize outliers. 3.Use a calculator to find r. Interpret r, the correlation coefficient. 4.Know the basic properties of correlation: r measures the strength and direction of only straight-line relationships, r is always a number between -1 and 1; r = ± 1 only for perfect straight-line relationships; r moves away from 0 toward ± 1 as the straight-line relationship gets stronger.

9. Chapter 4: Describing Relationships 5. Find LSRL on the calculator. Write the equation statistics style. 6.Explain what the slope and y-intercept mean in context. 7.Use a regression equation to predict y for a given x. Recognize the danger of extrapolation. 8.Use a residual plot to examine how appropriate a regression model is. 9.Use r-squared to describe how much of the variation in y can be accounted for by a straight-line relationship with x. 10.Give plausible explanations for an observed association between two variables: direct cause and effect, the influence of lurking variables, or both.

10. Circle the Wagons!