A financial analyst is examining the relationship between stock prices and earnings per share. She chooses publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least-squares regression line to be =y+−0.2130.046x. This line, along with a scatter plot of her data, is shown below. Based on the sample data and the regression line, complete the following. (a)For these data, current stock prices that are less than the mean of the current stock prices tend to be paired with values for earnings per share that are ▼(Choose one) the mean of the values for earnings per share. (b)According to the regression equation, for an increase of one dollar in earnings per share, there is a corresponding ▼(Choose one) of 0.046 dollars in current stock price. (c)What was the observed current stock price (in dollars) when the earnings per share was 52.82 dollars? (d)From the regression equation, what is the predicted current stock price (in dollars) when the earnings per share is 52.82 dollars? (Round your answer to at least two decimal places.)
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
A financial analyst is examining the relationship between stock prices and earnings per share. She chooses publicly traded companies at random and records for each the company's current stock price and the company's earnings per share reported for the past 12 months. Her data are given below, with x denoting the earnings per share from the previous year, and y denoting the current stock price (both in dollars). Based on these data, she computes the least-squares regression line to be =y+−0.2130.046x. This line, along with a
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