age,gender,primetyp,PILSF_total_w1,educationavg,goalsavg,nounsavg,religionavg 18,1,1,16, XXXXXXXXXX, XXXXXXXXXX, XXXXXXXXXX,4 19,1,1,24, XXXXXXXXXX, XXXXXXXXXX, XXXXXXXXXX,4.5 18,1,1,28, XXXXXXXXXX,...

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age,gender,primetyp,PILSF_total_w1,educationavg,goalsavg,nounsavg,religionavg 18,1,1,16,2.666666667,4.285714286,3.538461538,4 19,1,1,24,3.444444444,3.428571429,3.230769231,4.5 18,1,1,28,2.555555556,2.857142857,2.923076923,3 18,1,1,25,3.555555556,4.857142857,3.153846154,3 18,1,1,22,4.666666667,4.571428571,3.384615385,3 19,1,1,24,4.444444444,5,3.692307692,5 19,1,1,26,4.777777778,4.714285714,3.615384615,4 19,1,1,21,4,4.285714286,3.076923077,4.5 20,1,1,24,4.111111111,4.285714286,3.846153846,3 19,1,1,22,3,3.857142857,3.153846154,3.5 19,1,1,21,3.444444444,4,3.384615385,2.5 20,1,1,28,3.666666667,4.571428571,2.846153846,3.5 18,1,1,24,3.666666667,4.571428571,3.538461538,3 19,1,1,25,3.888888889,4.571428571,3.307692308,4.5 18,1,1,22,4.222222222,4.571428571,3.307692308,3 19,1,1,24,3.222222222,3.714285714,3.230769231,3 18,1,1,22,3.888888889,4.714285714,3.153846154,3.5 18,1,2,22,2.666666667,2.285714286,3.076923077,4 19,1,2,16,2.666666667,2.714285714,3.230769231,2.5 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18,2,1,22,3.777777778,4.714285714,3.923076923,5 23,2,1,23,4.555555556,4.857142857,2.692307692,4 41,2,1,26,4.777777778,5,4.076923077,5 18,2,1,25,4.777777778,5,3.307692308,3 21,2,1,24,4.666666667,4.571428571,4.153846154,4 18,2,1,28,5,5,4,4 18,2,1,25,4.666666667,5,3.769230769,5 20,2,1,20,4.111111111,4.714285714,3.615384615,2.5 19,2,1,21,3.555555556,3.571428571,3.307692308,2.5 22,2,1,22,4.444444444,4.571428571,4.153846154,4.5 18,2,1,23,3.111111111,4.142857143,3.538461538,4 19,2,1,22,3.111111111,4.285714286,3.153846154,3.5 18,2,1,28,4.777777778,4.714285714,3.538461538,4 20,2,1,28,4.444444444,5,3.769230769,5 19,2,1,25,3.666666667,4.857142857,3.692307692,4 19,2,1,18,3.555555556,4.571428571,3.538461538,4.5 19,2,1,18,4.444444444,4.714285714,3.076923077,4.5 18,2,1,19,3,4,3.384615385,3.5 20,2,1,21,4.333333333,4.428571429,3.307692308,4.5 19,2,1,27,4.333333333,4.857142857,3.923076923,3 18,2,1,27,4.222222222,4.857142857,4.230769231,3.5 18,2,1,25,4.555555556,4.571428571,3.384615385,3.5 18,2,1,19,4,4,3.384615385,4 18,2,1,25,3.444444444,4.428571429,3.307692308,3 19,2,1,25,4.666666667,4.857142857,3.538461538,4 19,2,1,25,4.333333333,5,3.153846154,4 18,2,1,25,3.555555556,4.571428571,3.153846154,3.5 18,2,1,22,4.111111111,4.428571429,3.538461538,4.5 21,2,1,27,4.555555556,4.857142857,3.769230769,3.5 19,2,1,22,4.555555556,4.714285714,3.461538462,4 18,2,1,27,4.222222222,4.857142857,4,3.5 19,2,1,20,3.555555556,3.714285714,3.076923077,3 18,2,1,28,4,4.857142857,3.230769231,3.5 19,2,1,26,3.333333333,3.857142857,3.461538462,3 20,2,1,22,3.333333333,4.142857143,3,3.5 18,2,1,25,4.444444444,4.857142857,3.384615385,3.5 19,2,1,18,4.444444444,4.714285714,3.384615385,4 21,2,1,22,4.111111111,4.571428571,3.461538462,3 22,2,1,19,3.333333333,4,3.307692308,3 18,2,1,17,3.777777778,4,3.230769231,3 18,2,1,25,4.222222222,4.428571429,3.384615385,4 20,2,1,23,3.666666667,4.428571429,3.384615385,4 18,2,1,25,3.888888889,4.571428571,3.615384615,4 18,2,2,23,4.666666667,3.571428571,3.153846154,5 21,2,2,27,5,5,5,5 20,2,2,21,4.222222222,4.571428571,3.538461538,2 21,2,2,28,4.333333333,4.428571429,1.615384615,3 18,2,2,15,5,5,2.076923077,4.5 19,2,2,24,4,4.142857143,1.846153846,4.5 19,2,2,21,2.888888889,3.428571429,2.153846154,3 19,2,2,25,4.888888889,4.428571429,2.076923077,4 43,2,2,23,4.555555556,5,3.384615385,2.5 21,2,2,28,4.555555556,4.428571429,1.923076923,4 18,2,2,27,4.777777778,5,2.384615385,5 19,2,2,25,4.777777778,4.428571429,2.692307692,2.5 19,2,2,27,4.666666667,4.714285714,2.230769231,3 19,2,2,23,4.888888889,5,4.307692308,5 18,2,2,22,4.333333333,4.714285714,3,2.5 18,2,2,24,4.888888889,4.857142857,4.153846154,3.5 20,2,2,25,5,5,3.692307692,3 21,2,2,20,3.888888889,4.285714286,3.307692308,5 20,2,2,15,4.777777778,4.285714286,3.384615385,3 18,2,2,22,4.222222222,4,3.153846154,2.5 18,2,2,26,3.888888889,4.714285714,3.538461538,4 19,2,2,19,4.888888889,4.571428571,2.384615385,4 18,2,2,22,4.777777778,4.428571429,3.538461538,5 18,2,2,26,4.888888889,5,4.230769231,4 19,2,2,28,4.777777778,4.857142857,4.230769231,4.5 18,2,2,24,4.777777778,5,4.230769231,4.5 19,2,2,17,3.555555556,4.285714286,3,4 20,2,2,26,4.222222222,4.142857143,3,2.5 19,2,2,24,5,5,4.153846154,4 19,2,2,25,5,4.857142857,2.923076923,5 19,2,2,26,4.111111111,4.571428571,3.153846154,5 18,2,2,22,4.555555556,4.857142857,2.692307692,3 20,2,2,24,4.444444444,4.428571429,2.384615385,4.5 18,2,2,22,4,4.714285714,3.384615385,4.5 18,2,2,22,4,4.714285714,2.692307692,4 21,2,2,15,4.444444444,4.285714286,2.538461538,4.5 18,2,2,23,4.777777778,5,2.538461538,4 18,2,2,25,4.444444444,4.428571429,3.384615385,5 18,2,2,23,4.888888889,5,3,5 19,2,2,20,4.111111111,4.285714286,3.769230769,3.5 18,2,2,23,4.888888889,4.714285714,2.692307692,4.5 22,2,2,17,4.666666667,4.142857143,3.192307692,4 19,2,2,22,4.444444444,5,3.769230769,4.5 19,2,2,28,4.555555556,4.714285714,2.769230769,3 18,2,2,25,4.444444444,4.857142857,3.230769231,5 18,2,2,25,4.666666667,4.571428571,2.461538462,3.5 18,2,2,26,4.222222222,4.857142857,3.461538462,4.5 21,2,2,25,4,4.428571429,2.769230769,4.5 18,2,2,26,4.666666667,4.857142857,3.615384615,5 22,2,2,23,4.222222222,3.857142857,3.153846154,3.5 18,2,2,13,4,3.857142857,2.615384615,3 18,2,2,20,4.888888889,5,3.923076923,4 19,2,2,23,4.111111111,4.714285714,3,3.5 18,2,2,26,4.888888889,4.571428571,3.615384615,4 19,2,2,24,4.444444444,5,3.230769231,4.5 18,2,2,20,4.222222222,4.714285714,3,4.5 21,2,2,24,4.333333333,4.714285714,3.692307692,4 21,2,2,20,4.444444444,5,3.384615385,4 18,2,2,19,4.777777778,5,3.769230769,4.5 18,2,2,25,4.777777778,5,3.153846154,3.5 18,2,2,23,4.555555556,4.714285714,2.692307692,3.5 19,2,2,22,4.555555556,4.857142857,3.692307692,4.5 18,2,2,24,4.666666667,4.571428571,3.538461538,3.5 19,2,2,25,4.777777778,4.571428571,3.384615385,4.5 19,2,2,22,4.777777778,4.714285714,2.769230769,4 19,2,2,21,4.333333333,4.428571429,3.461538462,4.5 19,2,2,24,4,4.285714286,3.307692308,4 18,2,2,27,4.666666667,4.714285714,3.538461538,4.5 18,2,2,25,4.888888889,5,3.384615385,4.5 18,2,2,24,4.777777778,5,3.384615385,4.5 21,2,2,26,4.444444444,4.714285714,3.230769231,4.5 19,2,2,20,4.777777778,4.857142857,3.538461538,4 22,2,2,28,4.555555556,4.571428571,3.153846154,3.5 19,2,2,24,4.666666667,4.714285714,3,4 Assignment 2 PSYC316. Fall 2021 Assignment 2: Regressions Subjects took two questionnaires. First, they filled out a survey rating different words on their meaningfulness or pleasantness. Scores for both questionnaires were rated on a Likert scale from 1 (not meaningful, not pleasant) to 5 (very meaningful, very pleasant). Once the ratings were obtained, the researchers grouped these words into sets based on previous research. This produced four sets of words: Education words, Goal words, Noun words, and Religion words. The data set provided contains the average ratings for the words by set. These same participants then completed a “purpose in life questionnaire” (PIL), where the scores on questions were totaled for each participant. Include the appropriate output into this document while answering the questions. You can also upload your excel file for data screening, which will help us figure out what happened if your answers are incorrect. In the assignment, you will delete people (the whole row) if they should be excluded. IV: • Control variables: Age, gender (1=female, 2=male) • Experimental manipulation: priming type (1=meaningful, 2=pleasantness) • Education words averaged (i.e., accomplish, College, Degree
Answered Same DayOct 29, 2021

Answer To: age,gender,primetyp,PILSF_total_w1,educationavg,goalsavg,nounsavg,religionavg 18,1,1,16, XXXXXXXXXX,...

Mohd answered on Oct 29 2021
129 Votes
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10/28/2021
library(readr)
library(magrittr)
library(dplyr)
library(ggplot2)
library(rmarkdown)
library(MASS)
library(skimr)
library(readr)
data_a2 <- read_csv("data/data_a2.csv")
Include the appropriate output into this document while answering the questions. You can also u
pload your excel file for data screening, which will help us figure out what happened if your answers are incorrect. In the assignment, you will delete people (the whole row) if they should be excluded. IV: • Control variables: Age, gender (1=female, 2=male) • Experimental manipulation: priming type (1=meaningful, 2=pleasantness) • Education words averaged (i.e., accomplish, College, Degree, Education, Grades, Graduate, School, Teacher, Undergrad, University) • Goals words averaged (i.e., achieve, ambition, become, goals, progress, success) • Nouns words averaged (i.e., everything, know, lot, many, mind, much, right, some, something, thing, time, what, when) • Religion words averaged (i.e., serve, glorify) DV: • PIL total – scores on the purpose in life questionnaire Research Question: The hypothesis is that word ratings would predict scores on the PIL questionnaire in some way. For the analyses, first control for demographics (step 1), then priming type (step 2), then use the average of their word ratings (step 3) to predict the PIL total (DV). Accuracy: 1. Include a summary table showing you do/do not have out of range scores.
summary(data_a2$PILSF_total_w1)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 10.00 21.00 24.00 23.03 25.00 28.00
boxplot(data_a2$PILSF_total_w1)
data_df<-data_a2%>%
filter(PILSF_total_w1>=18)
boxplot(data_df$PILSF_total_w1)
1. If necessary, fix the out of range scores.
1. Indicate what the problems were in the dataset.
summary(data_a2)
## age gender primetyp PILSF_total_w1
## Min. :18.00 Min. :1.000 Min. :1.000 Min. :10.00
## 1st Qu.:18.00 1st Qu.:2.000 1st Qu.:1.000 1st Qu.:21.00
## Median :19.00 Median :2.000 Median :2.000 Median :24.00
## Mean :19.38 Mean :1.792 Mean :1.566 Mean :23.03
## 3rd Qu.:20.00 3rd Qu.:2.000 3rd Qu.:2.000 3rd Qu.:25.00
## Max. :43.00 Max. :2.000 Max. :2.000 Max. :28.00
## educationavg goalsavg nounsavg religionavg
## Min. :2.556 Min. :2.286 Min. :1.615 Min. :1.500
## 1st Qu.:3.889 1st Qu.:4.286 1st Qu.:3.000 1st Qu.:3.000
## Median :4.333 Median :4.571 Median :3.308 Median :4.000
## Mean :4.222 Mean :4.514 Mean :3.271 Mean :3.808
## 3rd Qu.:4.667 3rd Qu.:4.857 3rd Qu.:3.577 3rd Qu.:4.500
## Max. :5.000 Max. :5.000 Max. :5.000 Max. :5.000
1. Make all out of range values NA.
sum(is.na(data_a2))
## [1] 0
1. Include a summary table showing that you fixed the accuracy issues (i.e. rerun the...
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