(b) Compute the value of the correlation coefficient. Round your answer to at least three decimal places. r3= Part 3 of 5 (c) State the hypotheses. H: ロ=ロ ロロ ロ<ロ H : Part: 3/5 Part 4 of 5 (d) Test the significance of the correlation coefficient at a=0.05, using The Critical Values for the PPMC Table. Critical value(s): ±

Q1-Q5: a) Find the value of the line ar correlation coefficient r b) Find the critical values ofr from table A-6 using x= 0.05 c) Determine whether there is sufficient evidence to support a claim of a linear correlation Between the two variables. d) Find the regression equation, e) Letting the first variable be the predietor (x) variable, find the indicated predieted value. Q1. Altitude and Temperature: Altitude 3 10 14 22 28 31 33 ( thousands of feet) Temperature ( °F) 57 37 24 -5 -30 -41 -54 At 6.327 thousand feet, the author recorded the temperature. Find the best predieted temperature at that altitude. How does the result compare to the actual recorded value of 48°F?

Determining significance of the correlation coefficient. Step 1: State the hypotheses. Ho: p = 0; H₁: p = 0 Step 2: Find the critical values. Two-tailed, Table F Step 3: Compute the test value. t = r freedom d.f. = n-2 Step 4: Make the decision. Step 5: Summarize the results. 54 n-2 1-2 with degrees of For each exercise, draw the scatter plot, determine if r is statistically significant, find the equation of the regression line and find the y'value for the specified xvalue. Remember that no regression should be done when ris not significant. 8.2 1. New Broadway productions and seasonal attendance are as follows. No. Of 67 62 54 61 Productions Attendance 7.4 (million) Find y' when x = 48 new productions. n(Exy) - (Ex) (Ey) √[n(Ex²) - (Ex)²][n(Ey²) — (Ey)²] CALC: STAT →Tests →LinRegTTest 7.1 r = 8.8 9.6 60 11 50 8.4 37 7.4

Please finish the R code to conduct the hypothesis testing. I need the code for the p-value for the F test, tstat, and the p value for tstat. Write the conclusion to the hypothesis testing once completed.  # Example 11.1# simple linear regression# H0: B=0 (there is no linear correlation)# H1: B≠0 (there is a linear correlation)data <- data.frame(estriol=c(7,9,9,12,14,16,16,14,16,16,17,19,21,24,15,16,17,25,27,15,15,15,16,19,18,17,18,20,22,25,24),birthweight=c(25,25,25,27,27,27,24,30,30,31,30,31,30,28,32,32,32,32,34,34,34,35,35,34,35,36,37,38,40,39,43))linear <- lm(birthweight~estriol, data = data)summary(linear)# This gives the results for a and b and the F statistic. # Call:#   lm(formula = birthweight ~ estriol, data = data)# # Residuals:#   Min      1Q  Median      3Q     Max # -8.1200 -2.0381 -0.0381  3.3537  6.8800 # # Coefficients:#   Estimate Std. Error t value Pr(>|t|)    # (Intercept)  21.5234     2.6204   8.214 4.68e-09 ***#   estriol       0.6082     0.1468   4.143…

You run a regression analysis on a bivariate set of data (n = 110). You obtain the regression equation y = – 3.546x 4.393 0.248 (which is significant at a = 0.01). You want to predict with a correlation coefficient of r = - what value (on average) for the explanatory variable will give you a value of 100 on the response variable. What is the predicted explanatory value? X = (Report answer accurate to one decimal place.)

Book Pages (x) Price (y) A 500 $7.10 B 700 7.60 C 750 9.10 D 590 6.60 E 560 7.60 F 650 7.10 G 475 5.10 (a) Develop a least squares estimated regression line. (Round your numerical values to three decimal places.) (b) Compute the coefficient of determination. (Round your answer to four decimal places.) Explain its meaning. (Give your answer as a percent. Round your answer to two decimal places.) The value of the coefficient of determination tells us that % of the variability in |--?-- ☑ has been explained by the least squares regression equation.

My solution is incorrect

The linear correlation coefficient is r=0.9780.978. ​(Round to three decimal places as​ needed.) Determine the null and alternative hypotheses.     H0​:ρ   ____   ______   H1​:ρ  _____  _______ (Type integers or decimals. Do not​ round.) The test statistic is t= ​(Round to two decimal places as​ needed.)   The​ P-value is ​(Round to three decimal places as​ needed.)   Because the​ P-value of the linear correlation coefficient is less than or equal to:   the significance​ level, there is: sufficient evidence to support the claim that there is a linear correlation between bill amounts and tip amounts. If everyone were to tip with the same​ percentage, then r= ​(Round to three decimal places as​ needed.)

find the linear correlation coefficent and the critical value(s)

ou run a regression analysis on a bivariate set of data (n=20n=20). With ¯x=44.9x¯=44.9 and ¯y=42.7y¯=42.7, you obtain the regression equationy=3.921x+57.242y=3.921x+57.242with a correlation coefficient of r=0.264r=0.264. You want to predict what value (on average) for the response variable will be obtained from a value of 140 as the explanatory variable.What is the predicted response value?y = (Report answer accurate to one decimal place.)