For this assignment use the following datafile: data file

First, participants were asked to rate a series of 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.

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 - sum of scores on the purpose in life questionnaire

Research Question:We would like to test whether word ratings predict scores on the PIL questionnaire, above and beyond control variables and experimental manipulation. HINT: To test this, you will need a regression model that includes demographics and priming type (the null model) as the sole predictors, as well as a second regression model that includes actual predictor variables in addition to the controls (the regression model).

Data below:

age (ordinal variable)	gender (nominal variable)	primetyp (nominal variable)	PILSF_total_w1 (scale variable)	educationavg (scale variable)	goalsavg (scale variable)	nounsavg (scale variable)	religionavg (scale variable)
18	1	1	16	2.666667	4.285714	3.538462	4
19	1	1	24	3.444444	3.428571	3.230769	4.5
18	1	1	25	3.555556	4.857143	3.153846	3
18	1	1	22	4.666667	4.571429	3.384615	3
19	1	1	24	4.444444	5	3.692308	5
19	1	1	26	4.777778	4.714286	3.615385	4
19	1	1	21	4	4.285714	3.076923	4.5
20	1	1	24	4.111111	4.285714	3.846154	3
19	1	1	22	3	3.857143	3.153846	3.5
19	1	1	21	3.444444	4	3.384615	2.5
20	1	1	28	3.666667	4.571429	2.846154	3.5
18	1	1	24	3.666667	4.571429	3.538462	3
19	1	1	25	3.888889	4.571429	3.307692	4.5
18	1	1	22	4.222222	4.571429	3.307692	3
19	1	1	24	3.222222	3.714286	3.230769	3
18	1	1	22	3.888889	4.714286	3.153846	3.5
18	1	2	22	2.666667	2.285714	3.076923	4
19	1	2	16	2.666667	2.714286	3.230769	2.5
18	1	2	20	3.111111	2.571429	2.384615	4
21	1	2	24	4.888889	4.428571	2	3
19	1	2	28	4.555556	5	2.538462	5
18	1	2	19	4.111111	4.571429	4.076923	4
18	1	2	20	4.444444	4.142857	3.615385	3
32	1	2	18	3.888889	4	3.769231	3.5
24	1	2	24	4.555556	5	2.923077	3
18	1	2	26	4.888889	5	3.076923	3
21	1	2	21	3.555556	3.857143	3.076923	3
22	1	2	24	4.555556	4.714286	2.538462	3.5
20	1	2	22	3.888889	4.142857	3.307692	4
21	1	2	20	4.222222	4.571429	3	3.5
18	1	2	23	4.444444	4.428571	2.846154	3.5
18	1	2	20	4.333333	4.428571	2.923077	3.5
19	2	1	26	5	5	5	5
18	2	1	24	3	3.428571	3.307692	1.5
18	2	1	22	3.111111	4.714286	3.076923	3.5
20	2	1	22	4.777778	4.857143	3.692308	2.5
18	2	1	24	3.777778	4.857143	2.384615	3.5
18	2	1	27	3.888889	5	2.769231	4.5
18	2	1	21	2.777778	4	2.615385	3
18	2	1	23	4	3.714286	3.615385	3
18	2	1	22	3.777778	4.714286	3.923077	5
23	2	1	23	4.555556	4.857143	2.692308	4
41	2	1	26	4.777778	5	4.076923	5
18	2	1	25	4.777778	5	3.307692	3
21	2	1	24	4.666667	4.571429	4.153846	4
18	2	1	28	5	5	4	4
18	2	1	25	4.666667	5	3.769231	5
20	2	1	20	4.111111	4.714286	3.615385	2.5
19	2	1	21	3.555556	3.571429	3.307692	2.5
22	2	1	22	4.444444	4.571429	4.153846	4.5
18	2	1	23	3.111111	4.142857	3.538462	4
19	2	1	22	3.111111	4.285714	3.153846	3.5
18	2	1	28	4.777778	4.714286	3.538462	4
20	2	1	28	4.444444	5	3.769231	5
19	2	1	25	3.666667	4.857143	3.692308	4
19	2	1	18	3.555556	4.571429	3.538462	4.5
19	2	1	18	4.444444	4.714286	3.076923	4.5
18	2	1	19	3	4	3.384615	3.5
20	2	1	21	4.333333	4.428571	3.307692	4.5
19	2	1	27	4.333333	4.857143	3.923077	3
18	2	1	27	4.222222	4.857143	4.230769	3.5
18	2	1	25	4.555556	4.571429	3.384615	3.5
18	2	1	19	4	4	3.384615	4
18	2	1	25	3.444444	4.428571	3.307692	3
19	2	1	25	4.666667	4.857143	3.538462	4
19	2	1	25	4.333333	5	3.153846	4
18	2	1	25	3.555556	4.571429	3.153846	3.5
18	2	1	22	4.111111	4.428571	3.538462	4.5
21	2	1	27	4.555556	4.857143	3.769231	3.5
19	2	1	22	4.555556	4.714286	3.461538	4
18	2	1	27	4.222222	4.857143	4	3.5
19	2	1	20	3.555556	3.714286	3.076923	3
18	2	1	28	4	4.857143	3.230769	3.5
19	2	1	26	3.333333	3.857143	3.461538	3
20	2	1	22	3.333333	4.142857	3	3.5
18	2	1	25	4.444444	4.857143	3.384615	3.5
19	2	1	18	4.444444	4.714286	3.384615	4
21	2	1	22	4.111111	4.571429	3.461538	3
22	2	1	19	3.333333	4	3.307692	3
18	2	1	17	3.777778	4	3.230769	3
18	2	1	25	4.222222	4.428571	3.384615	4
20	2	1	23	3.666667	4.428571	3.384615	4
18	2	1	25	3.888889	4.571429	3.615385	4
18	2	2	23	4.666667	3.571429	3.153846	5
21	2	2	27	5	5	5	5
20	2	2	21	4.222222	4.571429	3.538462	2
21	2	2	28	4.333333	4.428571	1.615385	3
18	2	2	15	5	5	2.076923	4.5
19	2	2	24	4	4.142857	1.846154	4.5
19	2	2	21	2.888889	3.428571	2.153846	3
19	2	2	25	4.888889	4.428571	2.076923	4
43	2	2	23	4.555556	5	3.384615	2.5
21	2	2	28	4.555556	4.428571	1.923077	4
18	2	2	27	4.777778	5	2.384615	5
19	2	2	25	4.777778	4.428571	2.692308	2.5
19	2	2	27	4.666667	4.714286	2.230769	3
19	2	2	23	4.888889	5	4.307692	5
18	2	2	22	4.333333	4.714286	3	2.5
18	2	2	24	4.888889	4.857143	4.153846	3.5
20	2	2	25	5	5	3.692308	3
21	2	2	20	3.888889	4.285714	3.307692	5
20	2	2	15	4.777778	4.285714	3.384615	3
18	2	2	22	4.222222	4	3.153846	2.5
18	2	2	26	3.888889	4.714286	3.538462	4
19	2	2	19	4.888889	4.571429	2.384615	4
18	2	2	22	4.777778	4.428571	3.538462	5
18	2	2	26	4.888889	5	4.230769	4
19	2	2	28	4.777778	4.857143	4.230769	4.5
18	2	2	24	4.777778	5	4.230769	4.5
19	2	2	17	3.555556	4.285714	3	4
20	2	2	26	4.222222	4.142857	3	2.5
19	2	2	24	5	5	4.153846	4
19	2	2	25	5	4.857143	2.923077	5
19	2	2	26	4.111111	4.571429	3.153846	5
18	2	2	22	4.555556	4.857143	2.692308	3
20	2	2	24	4.444444	4.428571	2.384615	4.5
18	2	2	22	4	4.714286	3.384615	4.5
18	2	2	22	4	4.714286	2.692308	4
21	2	2	15	4.444444	4.285714	2.538462	4.5
18	2	2	23	4.777778	5	2.538462	4
18	2	2	25	4.444444	4.428571	3.384615	5
18	2	2	23	4.888889	5	3	5
19	2	2	20	4.111111	4.285714	3.769231	3.5
18	2	2	23	4.888889	4.714286	2.692308	4.5
22	2	2	17	4.666667	4.142857	3.192308	4
19	2	2	22	4.444444	5	3.769231	4.5
19	2	2	28	4.555556	4.714286	2.769231	3
18	2	2	25	4.444444	4.857143	3.230769	5
18	2	2	25	4.666667	4.571429	2.461538	3.5
18	2	2	26	4.222222	4.857143	3.461538	4.5
21	2	2	25	4	4.428571	2.769231	4.5
18	2	2	26	4.666667	4.857143	3.615385	5
22	2	2	23	4.222222	3.857143	3.153846	3.5
18	2	2	20	4.888889	5	3.923077	4
19	2	2	23	4.111111	4.714286	3	3.5
18	2	2	26	4.888889	4.571429	3.615385	4
19	2	2	24	4.444444	5	3.230769	4.5
18	2	2	20	4.222222	4.714286	3	4.5
21	2	2	24	4.333333	4.714286	3.692308	4
21	2	2	20	4.444444	5	3.384615	4
18	2	2	19	4.777778	5	3.769231	4.5
18	2	2	25	4.777778	5	3.153846	3.5
18	2	2	23	4.555556	4.714286	2.692308	3.5
19	2	2	22	4.555556	4.857143	3.692308	4.5
18	2	2	24	4.666667	4.571429	3.538462	3.5
19	2	2	25	4.777778	4.571429	3.384615	4.5
19	2	2	22	4.777778	4.714286	2.769231	4
19	2	2	21	4.333333	4.428571	3.461538	4.5
19	2	2	24	4	4.285714	3.307692	4
18	2	2	27	4.666667	4.714286	3.538462	4.5
18	2	2	25	4.888889	5	3.384615	4.5
18	2	2	24	4.777778	5	3.384615	4.5
21	2	2	26	4.444444	4.714286	3.230769	4.5
19	2	2	20	4.777778	4.857143	3.538462	4
22	2	2	28	4.555556	4.571429	3.153846	3.5
19	2	2	24	4.666667	4.714286	3	4

How is the experiment done in JASP (explain the steps for descriptive statistics and multiple regression in JASP) (Note: when putting variables in JASP, covariates only support ordinal and scale and factor supports nominal)