PSY3801- Technical Assignment #3
Amanda Mae Woodward
2022-09-27
Overview
In this technical assignment, you will have the opportunity to apply the statistical knowledge that you’ve learned so far. The assignment will require you to use R Studio and import data. We recognize that doing both of these things may be new to you, so please make sure to ask your Lab TA, lecture TA, or Dr. Woodward any questions that may come up. Though the assignment is not due until Friday, it is strongly recommended that you start the assignment before then.
Getting Started:
- Download the data science salary data from Canvas.
- Open this data in R Studio (Hint: Select “Import Data” from the Global Environment and choose “From Text Base.” It will be helpful for you to know where your downloaded data went on your computer). Make sure you copy the import code into your R Markdown file.
#make sure that you copy the code to import data here (Hint: It includes read.csv() or read_csv). Note: understanding file paths isn't always the easiest, if you have ANY questions, please let us know
salaries <- read.csv("/cloud/project/salaries.csv")Understanding your Data:
- View your dataset in R Studio. (Do NOT write this code in your markdown file. just in the console.) What do you notice about it? (e.g. What variables are in the file?)
Write your text here The data looks like a reflection of the salaries of different jobs, along with their title, company size and location.
- Print the salary_in_usd column. (Hint: To print variable values, you only need to write data$column in a code chunk)
#your code here
salaries$salary_in_usd## [1] 79833 260000 109024 20000 150000 72000 190000 35735 135000 125000
## [11] 51321 40481 39916 87000 85000 8000 41689 114047 5707 56000
## [21] 43331 6072 47899 98000 115000 325000 42000 33511 100000 117104
## [31] 59303 70000 68428 450000 46759 74130 103000 250000 10000 138000
## [41] 45760 79833 50180 106000 112872 15966 76958 188000 105000 70139
## [51] 6072 91000 45896 54742 60000 148261 38776 118000 120000 138350
## [61] 110000 130800 21669 412000 45618 62726 49268 190200 105000 91237
## [71] 62726 42197 82528 150000 235000 53192 100000 5409 270000 80000
## [81] 79197 140000 54238 47282 153667 28476 59102 110000 170000 80000
## [91] 88654 76833 19609 276000 29751 89294 12000 450000 70000 95746
## [101] 75000 150000 36259 62000 73000 51519 187442 115000 150000 30428
## [111] 94564 113476 103160 12000 45391 225000 50000 40189 90000 200000
## [121] 60000 200000 50000 110037 10354 151000 120000 9466 20000 40570
## [131] 100000 49646 38400 24000 100000 90000 63711 77364 220000 80000
## [141] 135000 240000 150000 82500 100000 82744 62649 90000 153000 160000
## [151] 168000 150000 75774 13400 144000 127221 119059 423000 120000 125000
## [161] 230000 85000 28369 63831 130026 165000 80000 250000 55000 150000
## [171] 170000 82528 60000 235000 60757 174000 2859 40038 81000 5679
## [181] 22611 90734 26005 61896 12000 4000 50000 69741 76833 74000
## [191] 152000 21844 18000 174000 96113 147000 9272 24342 54094 90000
## [201] 61467 195000 37825 50000 160000 12901 200000 165000 20000 120000
## [211] 24823 56738 66022 5882 24823 185000 28609 90734 88654 140000
## [221] 46597 116914 33808 56256 225000 416000 87738 88654 135000 71786
## [231] 16228 256000 200000 200000 180000 110000 63810 46809 4000 21637
## [241] 103691 80000 110000 165000 18053 72212 36643 12103 96282 170000
## [251] 115000 90000 600000 28399 93000 99703 200000 173762 185000 141846
## [261] 130000 63831 16904 66265 25532 160000 93150 111775 28016 65013
## [271] 72500 18907 76833 85000 77684 100000 58000 55000 20171 59102
## [281] 112000 100000 69741 105000 69999 94665 102839 109000 51064 135000
## [291] 155000 120600 130000 90000 170000 150000 102100 84900 136620 99360
## [301] 117789 104702 146000 123000 52351 99000 116000 106260 126500 242000
## [311] 200000 65438 39263 78526 52351 165220 45807 120160 90320 181940
## [321] 132320 220110 160080 180000 120000 124190 130000 110000 170000 115500
## [331] 112900 90320 112900 90320 165400 132320 167000 243900 136600 109280
## [341] 128875 93700 224000 167875 175000 156600 108800 95550 113000 135000
## [351] 161342 137141 167000 123000 78526 65438 150000 211500 192400 90700
## [361] 130000 61300 130000 61300 160000 138600 136000 58000 135000 170000
## [371] 123000 189650 164996 54957 54957 118187 132000 165400 208775 147800
## [381] 136994 101570 128875 93700 79039 132320 37300 164000 155000 124333
## [391] 98158 120000 112900 90320 145000 105400 87932 117789 215300 158200
## [401] 209100 154600 115934 81666 175000 98158 58000 183600 52351 180000
## [411] 71982 45807 65949 49461 78526 58894 260000 60000 63900 160000
## [421] 112300 241000 159000 180000 80000 82900 100800 49461 140400 39263
## [431] 43966 32974 87932 76940 104702 91614 65949 87932 189650 164996
## [441] 43966 32974 98158 78526 215300 76940 209100 154600 180000 21983
## [451] 80000 78791 196979 120000 125000 37236 105000 87932 18442 31615
## [461] 58255 100000 54957 18442 162674 120000 144000 104890 100000 140000
## [471] 135000 50000 220000 140000 183228 91614 185100 220000 200000 120000
## [481] 120000 65000 324000 216000 210000 120000 230000 100000 100000 31875
## [491] 200000 75000 35590 78791 100000 153000 58035 165000 93427 52396
## [501] 62651 32974 40000 87425 115000 86703 75000 64849 120000 157000
## [511] 150000 70912 65000 71444 20000 48000 152500 68147 122346 380000
## [521] 69336 10000 20000 405000 135000 177000 78000 135000 100000 90320
## [531] 85000 75000 214000 192600 266400 213120 112900 155000 141300 102100
## [541] 115934 81666 206699 99100 130000 115000 110500 130000 99050 160000
## [551] 205300 140400 176000 144000 200100 160000 145000 70500 205300 140400
## [561] 205300 184700 175100 140250 116150 54000 170000 65438 80000 140000
## [571] 210000 140000 100000 69000 210000 140000 210000 150075 100000 25000
## [581] 126500 106260 220110 160080 105000 110925 45807 140000 99000 60000
## [591] 192564 144854 230000 150000 150260 109280 210000 170000 160000 130000
## [601] 67000 52000 154000 126000 129000 150000 200000- What type of information do you think is contained in this variable? (There is no one correct answer- I haven’t told you what these values mean. We want you to think critically about the information being provided to you.)
Your explanation goes here The information in this column seems to be the yearly salariesof the participants in this study.
Standard Scores and Z scores
- Create a graph of the salary_in_usd variable. (Hint: remember to use the ggplot library)
#your code goes here
library(ggplot2)
ggplot(salaries, aes(salary_in_usd))+geom_boxplot()
- calculate z scores for all salary_in_usd scores.
#your code goes here
scale(salaries$salary_in_usd)## [,1]
## [1,] -4.575271e-01
## [2,] 2.081565e+00
## [3,] -4.613862e-02
## [4,] -1.300753e+00
## [5,] 5.313358e-01
## [6,] -5.679175e-01
## [7,] 1.095055e+00
## [8,] -1.079000e+00
## [9,] 3.199409e-01
## [10,] 1.790110e-01
## [11,] -8.593465e-01
## [12,] -1.012114e+00
## [13,] -1.020077e+00
## [14,] -3.565226e-01
## [15,] -3.847086e-01
## [16,] -1.469869e+00
## [17,] -9.950901e-01
## [18,] 2.465047e-02
## [19,] -1.502184e+00
## [20,] -7.934054e-01
## [21,] -9.719495e-01
## [22,] -1.497040e+00
## [23,] -9.075727e-01
## [24,] -2.014997e-01
## [25,] 3.808110e-02
## [26,] 2.997609e+00
## [27,] -9.907072e-01
## [28,] -1.110343e+00
## [29,] -1.733138e-01
## [30,] 6.773275e-02
## [31,] -7.468562e-01
## [32,] -5.961035e-01
## [33,] -6.182577e-01
## [34,] 4.759233e+00
## [35,] -9.236387e-01
## [36,] -5.378994e-01
## [37,] -1.310348e-01
## [38,] 1.940635e+00
## [39,] -1.441683e+00
## [40,] 3.622199e-01
## [41,] -9.377176e-01
## [42,] -4.575271e-01
## [43,] -8.754266e-01
## [44,] -8.875582e-02
## [45,] 8.091211e-03
## [46,] -1.357604e+00
## [47,] -4.980445e-01
## [48,] 1.066869e+00
## [49,] -1.028488e-01
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## [78,] -1.506384e+00
## [79,] 2.222495e+00
## [80,] -4.551736e-01
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## [82,] 3.904059e-01
## [83,] -8.182372e-01
## [84,] -9.162681e-01
## [85,] 5.830148e-01
## [86,] -1.181301e+00
## [87,] -7.496889e-01
## [88,] -3.238386e-02
## [89,] 8.131956e-01
## [90,] -4.551736e-01
## [91,] -3.332128e-01
## [92,] -4.998061e-01
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## [94,] 2.307053e+00
## [95,] -1.163332e+00
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## [112,] 1.660338e-02
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## attr(,"scaled:center")
## [1] 112297.9
## attr(,"scaled:scale")
## [1] 70957.26- Save your standard scores (z scores) from #2 to the dataset. (Hint use data$column<- code to make standard scores)
#your code goes here
salaries$salary_in_usd <- scale(salaries$salary_in_usd)- make a graph of the zscores you’ve created
#your code goes here
ggplot(salaries, aes(salary_in_usd))+geom_boxplot()
- What do you notice about the two graphs?
write your answer here the x values on the graph make significantly more sense as standard scores ## calculating standard scores
- Aria wants to compare scores that students recieve on case study assignments. They know that the average case study score is 88.7 and has a standard deviation of 3.8. What is the standard score for a student who received a 93?
#(93-88.7)/3.8 = 1.131579- Interpret the z score (standard score) that you’ve calculated.
your answer goes here the standard score the student received was 1.131579 std. deviations above the average.
- On a standard normal distribution, what is the probability of obtaining a z score less than or equal to 2?
97.65%
- On a standard normal distribution, what is the z score associated
with the lower .05 of the distribution? What about the upper .05 of the
distribution?
lower= upper=