Consumer Reports evaluates products for consumers. The file...

Consumer Reports evaluates products for consumers. The file CompactSUV contains the data shown in Table 1.8 for 15 compact sports utility vehicles (SUVs) from the 2018 model line (Consumer Reports website): Make-manufacturer Model-name of the model Overall score-awarded based on a variety of measures, including those in this data set Recommended-Consumer Reports recommends the vehicle or not Owner satisfaction - satisfaction on a five-point scale based on the percentage of owners who would purchase the vehicle again $(--,-, 0,+,++)$ Overall miles per gallon-miles per gallon achieved in a 150 -mile test trip Acceleration $(0-60 \mathrm{sec})$ - time in seconds it takes vehicle to reach 60 miles per hour from a standstill with the engine idling a. How many variables are in the data set? b. Which of the variables are categorical, and which are quantitative? c. What percentage of these 15 vehicles are recommended? d. What is the average of the overall miles per gallon across all 15 vehicles? e. For owner satisfaction, construct a bar chart similar to Figure 1.4 . for the frequency distribution for acceleration using the following intervals: $7.0-7.9,8.0-8.9,9.0-9.9,$ and $10.0-10.9 .$ Construct a histogram similar to Figure 1.5

Consumer Reports evaluates products for consumers. The file CompactSUV contains the data shown in Table 1.8 for 15 compact sports utility vehicles (SUVs) from the 2018 model line (Consumer Reports website):
Make-manufacturer Model-name of the model
Overall score-awarded based on a variety of measures, including those in this data set Recommended-Consumer Reports recommends the vehicle or not Owner satisfaction - satisfaction on a five-point scale based on the percentage of owners who would purchase the vehicle again $(--,-, 0,+,++)$ Overall miles per gallon-miles per gallon achieved in a 150 -mile test trip Acceleration $(0-60 \mathrm{sec})$ - time in seconds it takes vehicle to reach 60 miles per hour from a standstill with the engine idling
a. How many variables are in the data set?
b. Which of the variables are categorical, and which are quantitative?
c. What percentage of these 15 vehicles are recommended?
d. What is the average of the overall miles per gallon across all 15 vehicles?
e. For owner satisfaction, construct a bar chart similar to Figure 1.4 .
for the frequency distribution for acceleration using the following intervals:
$7.0-7.9,8.0-8.9,9.0-9.9,$ and $10.0-10.9 .$ Construct a histogram similar to Figure 1.5

Key Concepts

Variable Identification

This concept involves determining and counting the number of variables in a data set. Variables are the individual characteristics or attributes that are recorded for each subject in the study. Accurately identifying variables is fundamental in data analysis and helps in further categorizing them, designing appropriate summaries, and selecting proper visualization techniques.

Categorical versus Quantitative Variables

Understanding the distinction between categorical and quantitative variables is crucial in statistics. Categorical variables represent qualities or labels (such as manufacturer names or recommendation statuses), whereas quantitative variables are numerical and can be measured on a numerical scale (like overall scores, miles per gallon, or acceleration times). This distinction informs the types of analyses and visualizations that are appropriate for each type of data.

Descriptive Statistics

Descriptive statistics involves summarizing and describing the main features of a data set through metrics like percentages, means (averages), medians, and ranges. Calculating a percentage, such as the proportion of vehicles recommended, or the average miles per gallon across vehicles, provides a quick insight into the central tendencies and distributions within the data set.

Frequency Distribution

Frequency distribution is a method to organize and display data so that the number of observations falling within specific intervals (or categories) is easily seen. It is essential for understanding how data values are spread out, and it serves as the basis for creating effective visual representations like bar charts or histograms.

Data Visualization - Bar Chart and Histogram

Data visualization techniques such as bar charts and histograms are tools to graphically represent the frequency distribution of data. Bar charts are typically used to display the frequency of categorical variables or discrete data, while histograms are used for continuous quantitative data that are divided into interval bins. These visualizations help in quickly interpreting the data trends and patterns.

Transcript

00:01 Okay, today we're going to be going over question 25.

00:04 So this consumer reports evaluate products for consumers.

00:06 The file compact suv contains the data shown in table 1 .8, 415 compact sports utility vehicles for the 2018 model.

00:17 And it goes through, then there's the table.

00:21 So how many variables are in the dataset? so your variables are going to be the, in this case, the different categories that there are is going to be a different variable.

00:41 So for each car, how many different values are you gathering per car, which is, in this case, seven different values.

00:51 So you have seven variables.

00:55 B.

00:56 B says which of the following are categorical and which one is quantitative.

01:00 So categorical, you could think of the ones that are in categories.

01:07 Which in this case are going to be your make, your model, your whether it's recommended, and owner satisfaction.

01:34 Your quantitative is going to be your numerical.

01:46 So the numerical ones are going to be your overall score, your overall miles per gallon, and your acceleration.

02:09 It says what percentage of the 15 vehicles are recommended? so you look on the column for recommended.

02:29 And you see there are seven yeses out of year 15.

02:32 Seven out of 15 is 46%.

02:40 Next up is d.