.importing data
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Timestamp Institution
1 04/11/2024 11:42 UR
2 04/11/2024 11:42 UR
3 04/11/2024 11:43 UR
4 04/11/2024 11:43 UR
5 04/11/2024 11:43 UR
6 04/11/2024 11:44 AIMS
Background
1 Data Science
2 Data Science
3 Applied Statistics
4 Bachelor in Science of Quantity Surveying and construction management
5 Data science
6 Data science
Completed.Program Gender Date.of.Birth
1 Master's degree Female 02/04/1986
2 Master's degree Male 08/04/1990
3 Bachelor's degree Male 17/08/2000
4 Bachelor's degree Male 01/01/2000
5 Master's degree Female 23/10/1994
6 Master's degree Female 01/01/1999Quadratic equation: \[ax^2+bx+c\] whereby \[a,b,c\] are real numbers
Equation of normal distribution: \[p(x; \mu, \sigma) = \frac{1}{\sigma \sqrt{2 \pi}} e^{\frac{-(x-\mu)^2}{2 \sigma^2}}\]
data.Institution
1 UR
2 UR
3 UR
4 UR
5 UR
6 AIMS
7 UR
8 UR
9 UR
10 UR
11 UR
12 UR
13 UR
14 UR
15 UR
16 UR
17 UR
18 UR
19 UR
20 UR
21 UR
22 UR
23 UR
24 University of Kerala
25 UR
26 EAUR
27 UR
28 UR
29 UTB
30 EAUR
31 UTB
32 UR
33 UR
34 UR
35 AIMS Rwanda
36 UTB
37 INES Ruhengeri
38 INES
39 INES
40 UR
41 UR
42 UR
43 AUCA
44 UR
45 UR
46 UR
47 UR
48 UR
49 UR
50 UR
51 UR data.c.1.6...3.
1 Data Science
2 Data Science
3 Applied Statistics
4 Bachelor in Science of Quantity Surveying and construction management
5 Data science
6 Data science.Data filtering
[1] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[4] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[7] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[10] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[13] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[16] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[19] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[22] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[25] "Bachelor's degree" "Bachelor's degree" Timestamp Institution
3 04/11/2024 11:43 UR
4 04/11/2024 11:43 UR
7 04/11/2024 11:45 UR
8 04/11/2024 11:46 UR
11 04/11/2024 11:48 UR
12 04/11/2024 11:48 UR
13 04/11/2024 11:48 UR
14 04/11/2024 11:49 UR
17 04/11/2024 11:49 UR
20 04/11/2024 11:50 UR
22 04/11/2024 11:51 UR
23 04/11/2024 11:51 UR
25 04/11/2024 11:55 UR
28 04/11/2024 11:57 UR
30 04/11/2024 11:57 EAUR
31 04/11/2024 11:58 UTB
33 04/11/2024 12:01 UR
36 04/11/2024 12:05 UTB
43 06/11/2024 9:26 AUCA
44 06/11/2024 9:56 UR
45 06/11/2024 10:08 UR
46 06/11/2024 10:26 UR
47 07/11/2024 9:04 UR
48 07/11/2024 9:16 UR
49 07/11/2024 9:37 UR
50 07/11/2024 10:24 UR [1] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[4] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[7] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[10] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[13] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[16] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[19] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[22] "Bachelor's degree" "Bachelor's degree" "Bachelor's degree"
[25] "Bachelor's degree" "Bachelor's degree"[1] 26.Data Visualization
No Yes
Female 29 27
Male 26 18
Pearson's Chi-squared test with Yates' continuity correction
data: table_data
X-squared = 0.27712, df = 1, p-value = 0.5986
nominal_predictor ordinal_predictor numeric_target categorical_target
1 C High 54.51504 No
2 C Medium 50.41233 Yes
3 C Medium 45.77503 Yes
4 B High 29.46753 No
5 C Low 61.31337 No
6 B Low 35.39360 Yes
Df Sum Sq Mean Sq F value Pr(>F)
nominal_predictor 2 199 99.26 0.949 0.391
Residuals 97 10150 104.63
Kruskal-Wallis rank sum test
data: numeric_target by nominal_predictor
Kruskal-Wallis chi-squared = 1.0731, df = 2, p-value = 0.5848
Df Sum Sq Mean Sq F value Pr(>F)
ordinal_predictor 2 47 23.68 0.223 0.801
Residuals 97 10301 106.19
Spearman's rank correlation rho
data: as.numeric(data$ordinal_predictor) and data$numeric_target
S = 165872, p-value = 0.9632
alternative hypothesis: true rho is not equal to 0
sample estimates:
rho
0.004667927 .Inferential statistics
.Contigency table
No Yes
High 20 12
Low 18 12
Medium 19 19
Pearson's Chi-squared test
data: data$ordinal_predictor and data$categorical_target
X-squared = 1.2648, df = 2, p-value = 0.5313 mpg cyl disp hp drat wt qsec vs am gear carb
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2
[1] "mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
[11] "carb"
Call:
lm(formula = mpg ~ cyl + drat, data = mt_data)
Residuals:
Min 1Q Median 3Q Max
-5.0845 -2.1061 -0.3432 1.8000 7.2096
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 28.7247 7.5921 3.783 0.000718 ***
cyl -2.4835 0.4472 -5.554 5.45e-06 ***
drat 1.8720 1.4937 1.253 0.220124
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 3.176 on 29 degrees of freedom
Multiple R-squared: 0.7402, Adjusted R-squared: 0.7223
F-statistic: 41.32 on 2 and 29 DF, p-value: 3.244e-09 [1] "coefficients" "residuals" "effects" "rank"
[5] "fitted.values" "assign" "qr" "df.residual"
[9] "xlevels" "call" "terms" "model" (Intercept) cyl drat
28.724665 -2.483514 1.871983 (Intercept) cyl drat
28.724665 -2.483514 1.871983 2.5 % 97.5 %
(Intercept) 13.196989 44.252341
cyl -3.398123 -1.568905
drat -1.182973 4.926938 lwr upr
Mazda RX4 14.477977 27.77065
Mazda RX4 Wag 14.477977 27.77065
Datsun 710 19.219731 32.77575
Hornet 4 Drive 12.777079 26.40150
Hornet Sportabout 8.048752 21.45785
Valiant 11.871128 26.10938
Duster 360 8.164141 21.56709
Merc 240D 18.857724 32.53873
Merc 230 19.367170 32.89039
Merc 280 14.507722 27.81578
Merc 280C 14.507722 27.81578
Merc 450SE 7.887116 21.31996
Merc 450SL 7.887116 21.31996
Merc 450SLC 7.887116 21.31996
Cadillac Fleetwood 7.583004 21.09992
Lincoln Continental 7.738422 21.20658
Chrysler Imperial 8.201490 21.60462
Fiat 128 19.678835 33.17776
Honda Civic 20.760316 35.27865
Toyota Corolla 19.922538 33.45821
Toyota Corona 18.881356 32.55253
Dodge Challenger 7.178054 20.86840
AMC Javelin 8.048752 21.45785
Camaro Z28 8.956619 22.72148
Pontiac Firebird 7.907806 21.33671
Fiat X1-9 19.678835 33.17776
Porsche 914-2 20.237900 33.92908
Lotus Europa 19.043043 32.65292
Ford Pantera L 9.387272 24.12537
Ferrari Dino 14.002813 27.19750
Maserati Bora 8.709559 22.25719
Volvo 142E 19.733333 33.23558
Thank you!!!!