This discussion is related with the topic of linear regression.Discuss any two variables that you believe are correlated.Clearly state your two variables and talk about how they are related or how one might lead to affect the other.Note that correlation does not imply causation.Two variables might be associated but this does not mean one causes the other.

Below is a scatterplot of a student’s SAT score and their college GPA. Suppose we want to decide if a student’s SAT score and their GPA are linearly related. What does this scatterplot tell us of the relationship? Support your answer. SAT score_GPA scatterplot.pdf

Find an interesting instance where two variables are strongly correlated, but share no causal link. Explain the situation, describe the variables, and explain why it doesn't make sense for the data to be correlated.

Two variables have a positive linear correlation. Is the slope of the regression line for the variables positive or negative? A. The slope is negative. As the independent variable increases the dependent variable also tends to increase. B. The slope is negative. As the independent variable increases the dependent variable tends to decrease. C. The slope is positive. As the independent variable increases the dependent variable also tends to increase. D. The slope is positive. As the independent variable increases the dependent variable tends to decrease.

KidsFeet Regression Line y-Intercept The KidsFeet dataframe contains data collected on 39 fourth grade students in Ann Arbor, MI, in October 1997. Two of the measurements taken on the children were the length in centimeters, (length), and width in centimeters, (width), of their longest foot. This data could be used to answer the following Research Question: How is the width of a fourth-grade student's foot related to the length? Which of the following is the y-intercept of the regression line? ## ## Simple Linear Regression## ## Correlation coefficient r = 0.6411 ## ## Equation of Regression Line:## ## length = 9.817 + 1.658 * width ## ## Residual Standard Error: s = 1.025 ## R^2 (unadjusted): R^2 = 0.411 ( ) 1.0248 ( ) 1.6576 ( ) 22.8153 ( ) 0.411 ( ) 9.8172 ( )0.6411

Consider the following passage: I ran a regression, with many variables to predict the result of another variable which was the murder rate. One can see that lots of things, can cause the murder rate to increase or decrease. I tried to account for all the important factors, and those factors are the SAT scores, unemployment rate, and international migration per 1,000. The SAT score is, average combined total score participants did on the SAT exam. The unemployment rate is, "a measure of the prevalence of unemployment and it is calculated as a percentage by dividing the number of unemployed individuals by all individuals currently in the labor force." (Wikipedia) International migration per 1,000 is, the number of people who come into a state from other countries per 1,000 people who live in the state. After I run the regression I will look at the t scores and p values and I should hopefully conclude that international migration does not cause crime. Which writing mistakes, if any, did...

The linear correlation between violent crime rate and percentage of the population that has a cell phone is −0.918 for years since 1995. Do you believe that increasing the percentage of the population that has a cell phone will decrease the violent crime​ rate? What might be a lurking variable between percentage of the population with a cell phone and violent crime​ rate? Question content area bottom Part 1 Will increasing the percentage of the population that has a cell phone decrease the violent crime​? Part 2 What might be a lurking variable between percentage of the population with a cell phone and violent crime​ rate?

Statistics Canada produces consumer price indexes for several different categories. Shown here are the percentage changes in consumer price indexes over a period of 22 years for durable goods, semidurable goods, and nondurable goods. Also displayed are the percentage changes in total goods price index. Use these data and multiple regression to develop a model that attempts to predict the total goods index by the other three variables. Comment on the result of this analysis. Year TotalGoods DurableGoods SemidurableGoods NondurableGoods 1 85.7 89.4 91.9 83.2 2 86.4 90.4 92.5 83.8 3 87.8 92.5 93.4 85.2 4 86.8 96.0 94.2 81.6 5 88.4 99.0 94.9 82.9 6 89.9 100.8 95.4 84.4 7 91.2 101.6 97.0 86.0 8 91.4 101.5 97.7 86.1 9 93.1 101.5 99.2 88.4 10 96.0 100.8 99.6 93.3 11 98.4 100.1 100.3 97.4 12 100 100 100 100 13 101.9 99.2...

The condition of an independent variable being correlated to one or more other independent variables is referred to as nonlinearity. statistical significance. multicollinearity. linearity.

we can conclude from correlation calculation that one variable caused the other variable. True or False.

Write down the fitted regression equation. If in the family, the primary caregiver is the biological mother, whose income is 50,000ドル and the youth live with the family most of the time. What predicted youth absent days in school?

Mrs. Kelley noticed a strong positive correlation between the hours students said that they studied for a test and their scores on the test. Which fact would lead you to believe that the relationship between studying and test scores is one of causation and not correlation?

The equation of a regression line, unlike the correlation, depends on the units we use to measure the explanatory and response variables. Here is the data on percent body fat and preferred amount of salt. Preferred amountof salt x 0.2 0.3 0.4 0.5 0.6 0.8 1.1 Percent body fat y 20 31 22 29 39 22 30 In calculating the preferred amount of salt, the weight of the salt was in milligrams. (a) Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in milligrams. (Round your answers to one decimal place.) ŷ = ? + ? x (b) A mad scientist decides to measure weight in tenths of milligrams. The same data in these units are as follows. Preferred amountof salt x 2 3 4 5 6 8 11 Percent body fat y 20 31 22 29 39 22 30 Find the equation of the regression line for predicting percent body fat from preferred amount of salt when weight is in tenths of milligrams. (Round your intercept to one decimal place and your slope...