An economist conducted a study of the possible association between weekly income and weekly grocery expenditures. The particular interest was whether higher income would result in shoppers spending more on groceries. A random sample of shoppers at a local supermarket was obtained, and a questionnaire was administered asking about the weekly income of the shopper’s family and the grocery bill for that week. The gender of the shopper was also obtained. The correlation was found to be r = 0.649. The standard deviation for the grocery bills was 37.87, and for income it was 309.04. Therefore, the slope of the least-squares line is: 1) 0.694 2) 0.0795 3) 0.0021 4) 5.296 Can you also please explain what least square line is? Why is it used instead of just sticking with regression lines?

The data provides information on life expectancy and the number of televisions per thousand people in a sample of 22 countries, as reported by the world almanac and book of facts 2006 A. Describe the Direction and strength of the correlation between the two variables B. Because of the association between the variables, someone might mistakenly conclude that simply sending televisions to the countries with the lower life expectancies would cause their inhabitants to live longer. Comment on this argument and the reason televisions are likely associated with longer life expectancies C. In general, if two variables are strongly associated, does it follow there must be a cause and effect relationship between them? Explain

Fifty-four wild bears were anesthetized, and then their weights and chest sizes were measured and listed in a data set. Results are shown in the accompanying display. Is there sufficient evidence to support the claim that there is a linear correlation between the weights of bears and their chest sizes? When measuring an anesthetized bear, is it easier to measure chest size than weight? If so, does it appear that a measured chest size can be used to predict the weight? Use a significance level of a = 0.05 Question: Determine the null and alternative hypotheses

Over the course of a year, you find a strong, positive correlation between ice cream sales and pool drownings in a medium-sized city. The mayor decides that to prevent drownings, ice cream will be banned within the city limits. However, you show the mayor that after controlling for the high temperature of each day, the correlation between ice cream sales and number of drownings drops to zero, because that relationship was actually: Group of answer choices   direct. interactive. mediating. spurious.

The number of wins for a high school football team is measured for the season. When the team plays at home, it is generally believed that they will win. Comparing the location of the game and the number of wins, a correlation coefficient of −0.11 is calculated. What would this imply about the football team winning at home?  The scatter plot would closely resemble a straight line with a negative slope. The data has a strong, negative correlation, and a causal relationship exists between the team playing at home and winning. The scatter plot would closely resemble a straight line with a negative slope. The data has a strong, negative correlation, but causation cannot be determined. The scatter plot would not be represented by a line of best fit with a negative slope. There is a weak correlation between the football team playing at home and winning, and a causal relationship exists between the team playing at home and winning. There is no causation and almost no correlation between the…

A survey was taken in 2018 that asked people about their saving habits. Researchers wanted to know if people who saved more also spent less. The scatterplot below shows their results when comparing two variables: the amount people reported that they put into savings each month, and the amount they reported that they spent on clothes. The researchers found the correlation coefficient for this data to be -0.239. Which of the following is true about these variables? a. There is no relationship between savings and money spent on clothes each month.b. There is a weak, positive linear relationship between savings and money spent on clothes each month.c. There is a perfect, negative linear relationship between savings and money spent on clothes each month.d. There is a weak, negative linear relationship between savings and money spent on clothes each month.

Would A be the answer?

An economist wants to determine whether there is a linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. The data are shown in the table below. c. Compute and interpret the correlation coefficient. d. Compute and interpret the coefficient of determination. e. Test for the significance of the linear relationship. Use a 0.05 level of significance. State your conclusion. Hint: Your conclusion is either of the following. • There is a significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. • There is no significant linear relationship between a country's gross domestic product (GDP) and carbon dioxide emissions. GDP 1.6 3.6 4.9 1.1 0.9 2.9 2.7 2.3 1.6 1.5 (trillion dollars) Carbon Dioxide Emissions 428.2 828.8 1214.2 444.6 264 415.3 571.8 454.9 358.7 573.5 (millions of metric tons)

Listed below are amounts of court income and salaries paid to the town justices. All amounts are in thousands of dollars. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value using a = 0.05. Is there sufficient evidence to conclude that there is a linear correlation between court incomes and justice salaries? Based on the results, does it appear that justices might profit by levying larger fines? Court Income 65.0 406.0 1566.0 1130.0 273.0 251.0 112.0 152.0 32.0 Justice Salary 31 45 92 58 44 60 24 25 18 B. Ho:p=0 A. Ho:p=0 H4:p#0 H:p>0 O C. Ho: p=0 H4:p<0 O D. Ho: p+0 H1:p=0 Construct a scatterplot. Choose the correct graph below. O A. В. O D. AJustice Salary 100- AJustice Salary 100- A Justice Salary 100- AJustice Salary 100- 50- 50- 50- 50- 0- 0- 0- 800 1600 800 1600 800 1600 800 1600 Court Income Court Income Court Income Court Income The linear correlation coefficient is r= (Round to three decimal places as needed.)

The data set provides a few measures from car models produced in the 1970s. We are focusing on Acceleration as the main outcome variable of interest in this analysis and we would like to examine other variables that may impact Acceleration. For exploring the relationships among the variables, run bivariate correlations for Engine, Horsepower, Weight, and Acceleration. Which variable has the strongest correlation with Acceleration (regardless of direction)?