472 CHAPTER 14 | Correlation and Regression 16. A set of n = 15 pairs of scores (X and Y values) pro- duces a regression equation of Ŷ = 2X + 6. Find the Y predicted Y value for each of the following X scores: 0, 2, 3, and -4.

Part 2: Regression Based on the correlation you ran to assess the relationship between # of exposures and perceived connection strength recalled, please carry out the steps to determine the equation for the regression line (or best-fitting line) а. Step 1: find the slope and y-intercept b. Step 2: Using these values, write the equation for the best fitting line, but make sure to write it in terms of the specific predictor (X) and predicted (Y) variables of interest. In other words, your equation should use variable names “# of exposures" and "perceived strength of connection rating", rather than X and Y. Using the regression equation, please predict the perceived connection strength score for a student exposed subliminally to the stranger's face seven times. Make sure to show your work с. d. Using the regression equation, draw the line of best fit on your scatterplot. Please use at least 3 points to anchor your line and show your work.

x = number of traffic tickets y = Cost of auto insurance A test of linear correlation between traffic tickets and insurance cost produces a linear correlation coefficient of r = 0.705.  Determine weather the regression line has a positive slope, or a negative slope

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What is the regression line

please answer the question and show work!! thank you also please make sure if writing on paper handwriting can be read thank you again :)

In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67  the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?

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Data Set: {(-3, 4), (-2, 3), (–1,3), (0, 7), (1, 5), (2, 6), (3, 1)} 1. The regression line is: y = when the 2. Based on the regression line, we would expect the value of response variable to be explanatory variable is 0. 3. For each increase of 1 in of the explanatory variable, we can expect a(n) in the response variable. 4. If x = -3.5, the y = This is an example of 5. The correlation coefficient is r = (Round to the nearest hundredth.) Check of