1a. Two variables have a positive linear correlation. Does the dependent variable increase or decrease as the independent variable increases? Explain.1b. Two variables have a negative linear correlation. Does the dependent variable increase or decrease as the independent variable increases?1c. Describe the range of values for the correlation coefficient, r.1d. What does the sample correlation coefficient r measure? Which value indicates a stronger correlation:r1 = 0.975 or r2 = -0.987. Explain.

use the sample data to construct a scatterplot. Use the first variable for the x-axis. Based on the scatterplot, what do you conclude about a linear correlation? FORECAST AND ACTUAL TEMPERATURES The table lists actual high temperatures and the high temperatures that were previously forecasted for these same days. The table includes data for ten different days near the author’s home. What does the result suggest about the accuracy of five-day predicted high temperatures?

For a set of correlated data, you calculate that the correlation coefficient (r) is -0.87. What percent of the variation in the dependent variable is explained by variation in the independent variable?  D2L requires decimal format. Convert your percentage to a decimal rounded to the third place. i.e. 51.2218% = 0.512.

Police sometimes measure shoe prints at crime scenes so that they can learn something about criminals. Listed below are shoe print lengths and heights of males. Construct a scatterplot, find the value of the linear correlation coefficient r, and find the P-value of r. Determine whether there is sufficient evidence to support a claim of linear correlation between the two variables. Use a significance level of a = 0.01. Click to view the data on shoe print lengths and heights. Construct a scatterplot. Choose the correct graf Shoe Print Lengths versus Heights of Males O B. OD. 210- 200- 190- 180- 170- 160- 20 24 28 32 36 40 Shoe Print Length (cm) 210 200 190 180 170 160 2101 200- Shoe Print 190- Length (cm) Height (cm) 176.0 180- 170- 160- 20 24 28 32 36 40 31.4 28.9 181.2 31.5 194.5 Shoe Print Length (cm) 32.3 172.9 30.4 174.0 The linear correlation coefficient is r= 32.9 190.5 (Round to three decimal places as needed.) 31.0 176.6 30.8 170.0 30.5 171.9 32.3 175.5 30.5 200.1 32.6 199.3…

You wish to determine if there is a linear correlation between the age of a driver and the number of driver deaths. The following table represents the age of a driver and the number of driver deaths per 100,000. Use a significance level of 0.05 and round all values to 4 decimal places. Driver Age Number of Driver Deaths per 100,000 21 22 39 21 38 18 23 22 79 36 42 22 65 29 Ho: p = 0 Ha: p= 0 Find the Linear Correlation Coefficient r = Find the p-value p-value = The p-value is O Less than (or equal to) a Greater than a The p-value leads to a decision to O Reject Ho O Accept Ho O Do Not Reject Ho The conclusion is O There is insufficient evidence to make a conclusion about the linear correlation between driver age and number of driver deaths. There is a significant linear correlation between driver age and number of driver deaths. O There is a significant negative linear correlation between driver age and number of driver deaths. O There is a significant positive linear correlation…

Statistics Question

2. Consider the following examples; would you expect the correlation coefficient to be close to 1, -1, or 0 (10 points):   The amount of time a dog is walked, and the number of hours the dog sleeps at night     Outdoor temperature and snowfall.         The length of time someone wears braces and their SAT scores.         3. The following graph represents the amount of time that someone studies for their final exam, and their final examination grade (HINT) (20 points).       Student 1 Student 2 Student 3 Student 4 Student 5 Student 6 Hours Studied 4 7 11 8 10 8 Final Exam Grade 55% 72% 88% 93% 91% 84%     Graph the following students as ordered pairs. Let the x-axis represent the hours studied, and the y-axis represent the students’ final exam grade.                         Circle the value that best represents the estimate of r: (circle one)   -0.99, -0.62, -0.30, 0, 0.30, 0.75, 0.85, 0.99

Which value of r indicates a stronger correlation: r= 0.841 or r= - 0.949? Explain your reasoning. Choose the correct answer below. O A. r= 0.841 represents a stronger correlation because -0.949 > 0.841|. B. r= - 0.949 represents a stronger correlation because -0.949|> |0.841|. C. r= - 0.949 represents a stronger correlation because 0.841 > - 0.949. D. r= 0.841 represents a stronger correlation because 0.841 > - 0.949.

a. Calculate the sample covariance.X Y-3 -913 95 12b. Calculate the sample correlation.c. Describe in words the information given to us by the sample covariance and by the sample correlation.Explain how the correlation gives us more precise information.d. Explain why the covariance is not a good measure of the strength of the linear relationship between the twovariables.e. Sketch a scatterplot of a bivariate dataset with a correlation of 0.9f. Sketch a scatterplot of a bivariate dataset with a correlation of -0.3.

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)