Analyzing a data set is typically done by first determining the type and visualizing it with graphs. To answer the questions below, you can use the data above. Let’s assume that all cars are included in the vehicle column. To create a random sample from 15 models of the previous set, use Table B’s random number. You can categorize each column by: car, city/highway, weight, number of cylinders and displacement man/autogHG NOX. The measurement can be categorical, quantitative, continuous or discrete. There are also four levels: ordinal, intervals, ratios, and nominal. A frequency distribution is created for the Man/Vehicle. A frequency distribution is created for the displacement variable. Include the cumulative frequency columns. Make a histogram of the displacement variable. Find out if there is any skewness left, right or symmetric. You can determine if your variability is high, low or both. Make a stemplot of the city. Determine whether the skewness in the city is left, right or symmetric. Determine if variability is low, high, or absent. Create a dotplot of the cylinder. Find the left, right and symmetric skewness. Determine if variability is high, low or absent.
Part II
In order to summarise a dataset we may have to find the measure of center or variation. Sometimes, it is necessary to calculate the quartiles in order to create boxplots. To summarize the data in the table, answer the questions below. The central measurement for column NOX (i.e. The mean, median, middlerange, and mode are the central measurements for column NOX (i.e. Use the center measures (mean. median. mode) to determine whether there is skewness within NOX. Determine the standard deviation, variance and interquartile range of the data set. How does this dataset’s standard deviation measure? What is the interquartile spread of this data? The HWY data can be used to answer these questions: Make a summary of five numbers. Create a boxplot. Find out if there are any outliers.
Part III
First, create a scatterplot to determine if there’s a linear relationship between automobile cylinders (Cylinders), and greenhouse gases emissions (GHG). Then compute the linear correlation coefficient. The regression procedure is applied if the linear relationship is clear. Answer the following questions: Make a scatter chart for GHG and cylinders. You can use your independent variable to represent cylinders. Your dependent variable is GHG. You should describe whether the linear relationship is positive or negative. Nonlinear: Yes, determine the coefficient for linear association between GHG and Cylinders. The coefficient of linear correlation is described. Are you optimistic? Are you feeling it is strong, weak or week? Use Table A6 to determine if the population has a link with cylinder or GHG.
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