JUST QUESTION 5 PLEASE Here are some formulas to help:ΣΧΣΥΣχ(ΣΥ)2SP-Σx -SS, = EY?.SP =SS =r =SS×SSYnUse this set of data to answer questions #1 - #17. You are conducting a correlational study.ParticipantYXYX2Y2A2413DE42F4G1. What is your null hypothesis? (in statistical notation)2. What is your null hypothesis? (in words)3. What is your alternative hypothesis? (in statistical notation)4. What is your alternative hypothesis? (in words)5. How many participants are in your study?

From the provided information, Participant X Y XY X2 Y2 A 2 4…

Answered: Use this set of data to answer…

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018

18th Edition

ISBN:9780079039897

Author:Carter

Publisher:Carter

Chapter4: Equations Of Linear Functions

Section4.5: Correlation And Causation

Problem 20HP

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Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
For the following exercises, use Table 4 which shows the percent of unemployed persons 25 years or older who are college graduates in a particular city, by year. Based on the set of data given in Table 5, calculate the regression line using a calculator or other technology tool, and determine the correlation coefficient. Round to three decimal places of accuracy

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