Base on the 3 pictures in the attachment. Answer the following question:

1.

a, Identify your explanatory and response variables and give units for each. 1 pt

b, Identify the strength (weak, moderate, or strong) and the direction (positive or negative) of the relationship in the data and discuss whether fitting a linear model would be reasonable (using the scatterplot and calculated correlation). 1 pt

c, Write the equation of the regression line for predicting your response variable using the R output with your variable names (assume a linear model is reasonable). Interpret the slope of your regression line in the context of your dataset.

Loading required package: usdata Warning message: package 'openintro' was built under R version 4.0.4 > data () > ?murders starting httpd help server . .. done > head (murders) # A tibble : 6 x 4 population perc pov perc unemp annual murders per mil 587000 16.5 6.2 11. 2 643000 20.5 6.4 13. 4 635000 26.3 9.3 40.7 692000 16.5 5.3 5.3 1248000 19.2 7.3 24.8 643000 16.5 5.9 12.7 names (murders) [1] "population" "perc_pov" "perc_unemp" [4] "annual murders per mil" > plot (x=murders$ perc_pov, y=murders$perc_unemp) > cor (murders$ perc_pov, murders$perc_unemp) [1] 0. 8154184 > linearmodel summary (linearmodel) Call : Im (formula = perc_pov ~ perc_unemp, data = murders) Residuals : Min 10 Median 30 Max -4.2483 -1.3088 0. 2422 1.7053 2. 3616 Coefficients: Estimate Std. Error t value Pr (>It|) (Intercept) 4. 5312 2.5778 1. 758 0. 0958 perc_unemp 2. 1902 0. 3665 5. 976 1. 18e-05 * * * Signif. codes: 0 1 * * *# 0. 001 1* *# 0.01 " *# 0. 05 . .' 0.1 . . 1 Residual standard error: 1. 928 on 18 degrees of freedom Multiple R-squared: 0. 6649, Adjusted R-squared: 0. 6463 F-statistic: 35. 72 on 1 and 18 DF, p-value: 1. 183e-05murders [openintro; R Documentation Data for 20 metropolitan areas. Description Population, percent in poverty, percent unemployment, and murder rate. Usage murders Format A data frame with 20 metropolitan areas on the following 4 variables. population Population. perc_por Percent in poverty. pere_unemp Percent unemployed. annual_murders_per_mil Number of marders per year per million people. Examples library(empletz) maplot (murders, aes (x = parc pov, y = annual_murders_per_mil]) . coon_point() . label N . "Percent in poverty" "Number of murders per year per million people"\f