Project Objective
To predict the factors which affect to Data Science's salary.
Step 0: Install and load required libraries
# Load necessary libraries
library(readxl) # For reading Excel files
library(dplyr) # For data manipulation
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(ggplot2) # For visualization
library(scales) # For formatting axes
library(tidyr) # For reshaping data
Step 1: Import and Clean Data
data <- read_excel(file.choose()) # Replace with the actual file path
data <- data %>%
rename(salary_usd = salary,
job_title = job_title,
experience_level = experience_level,
location = company_location,
company_size = company_size) %>%
mutate(experience_level = recode(experience_level,
"EN" = "Entry-Level",
"MI" = "Mid-Level",
"SE" = "Senior-Level",
"EX" = "Executive"))
Interpretation: The dataset has been cleaned and standardized. Variables like `experience_level' now have descriptive labels for better readability.
Step 2: Descriptive Statistics
summary(data)
## work_year experience_level employment_type job_title
## Min. :2020 Length:3755 Length:3755 Length:3755
## 1st Qu.:2022 Class :character Class :character Class :character
## Median :2022 Mode :character Mode :character Mode :character
## Mean :2022
## 3rd Qu.:2023
## Max. :2023
## salary_usd location company_size
## Min. : 5132 Length:3755 Length:3755
## 1st Qu.: 95000 Class :character Class :character
## Median :135000 Mode :character Mode :character
## Mean :137570
## 3rd Qu.:175000
## Max. :450000
Interpretation: - Senior-Level and Executive positions tend to have higher median salaries.
- Variations are also observed across job titles, locations, and company sizes.
Step 3: Visualization 1 - Salary by Job Title
# Filter top 10 job titles by frequency
top_titles <- data %>%
count(job_title, sort = TRUE) %>%
top_n(10, n) %>%
pull(job_title)
data_top_titles <- data %>%
filter(job_title %in% top_titles) %>%
group_by(job_title) %>%
filter(between(salary_usd, quantile(salary_usd, 0.25) - 1.5 * IQR(salary_usd),
quantile(salary_usd, 0.75) + 1.5 * IQR(salary_usd))) %>%
ungroup()
# Order job titles by median salary
job_title_order <- data_top_titles %>%
group_by(job_title) %>%
summarize(median_salary = median(salary_usd, na.rm = TRUE)) %>%
arrange(median_salary) %>%
pull(job_title)
data_top_titles$job_title <- factor(data_top_titles$job_title, levels = job_title_order)
# Plot
ggplot(data_top_titles, aes(x = job_title, y = salary_usd)) +
geom_boxplot(fill = "skyblue", outlier.shape = NA) +
scale_y_continuous(labels = dollar_format(prefix = "$", suffix = "k", scale = 1e-3),
breaks = seq(0, 400000, by = 50000)) +
labs(title = "Average Salary by Top Job Titles (Ascending Order)",
x = "Job Title",
y = "Salary (USD)") +
theme_minimal()
Interpretation: Certain job titles (e.g., "Principal Data Scientist") show consistently higher salaries. Specialization in these roles leads to greater compensation.
Step 4: Visualization 2 - Salary by Experience Level
data_experience <- data %>%
group_by(experience_level) %>%
filter(between(salary_usd, quantile(salary_usd, 0.25) - 1.5 * IQR(salary_usd),
quantile(salary_usd, 0.75) + 1.5 * IQR(salary_usd))) %>%
ungroup()
# Order experience levels by median salary
experience_order <- data_experience %>%
group_by(experience_level) %>%
summarize(median_salary = median(salary_usd, na.rm = TRUE)) %>%
arrange(median_salary) %>%
pull(experience_level)
data_experience$experience_level <- factor(data_experience$experience_level, levels = experience_order)
# Plot
ggplot(data_experience, aes(x = experience_level, y = salary_usd)) +
geom_boxplot(fill = "skyblue", outlier.shape = NA) +
scale_y_continuous(labels = dollar_format(prefix = "$", suffix = "k", scale = 1e-3),
breaks = seq(0, 400000, by = 50000)) +
labs(title = "Salary by Experience Level (Ascending Order)",
x = "Experience Level",
y = "Salary (USD)") +
theme_minimal()
Interpretation: Salaries increase significantly with experience, with Executive and Senior-Level role earning the most.
Step 5: Correlation Analysis
correlation_matrix <- cor(data %>% select_if(is.numeric), use = "complete.obs")
print(correlation_matrix)
## work_year salary_usd
## work_year 1.00000 0.22829
## salary_usd 0.22829 1.00000
Interpretation: - Positive correlation between `work_year` and `salary_usd` (0.23) suggests salaries increase slightly over time. Weak negative correlation between `remote_ratio` and `salary_usd` (-0.06) indicates fully remote roles may have slightly lower pay.
Step 6: Build the Regression Model
data$experience_level <- as.factor(data$experience_level)
data$job_title <- as.factor(data$job_title)
data$location <- as.factor(data$location)
data$company_size <- as.factor(data$company_size)
full_model <- lm(salary_usd ~ experience_level + job_title + location + company_size, data = data)
summary(full_model)
##
## Call:
## lm(formula = salary_usd ~ experience_level + job_title + location +
## company_size, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -142684 -29181 -4419 23731 375762
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 28144.6 46694.6 0.603
## experience_levelExecutive 86199.3 5815.4 14.823
## experience_levelMid-Level 20178.2 3429.4 5.884
## experience_levelSenior-Level 46081.8 3232.9 14.254
## job_titleAI Developer 127572.4 41183.0 3.098
## job_titleAI Programmer 14385.3 52177.7 0.276
## job_titleAI Scientist 50420.6 38581.4 1.307
## job_titleAnalytics Engineer 34034.5 37501.6 0.908
## job_titleApplied Data Scientist 68004.4 40508.7 1.679
## job_titleApplied Machine Learning Engineer 38736.2 50240.2 0.771
## job_titleApplied Machine Learning Scientist 32321.7 39688.9 0.814
## job_titleApplied Scientist 70095.2 37795.1 1.855
## job_titleAutonomous Vehicle Technician 47598.8 77472.2 0.614
## job_titleAzure Data Engineer 43555.0 61860.0 0.704
## job_titleBI Analyst 17616.4 40616.8 0.434
## job_titleBI Data Analyst 4169.6 39292.5 0.106
## job_titleBI Data Engineer -19854.1 60332.9 -0.329
## job_titleBI Developer 11148.4 39471.8 0.282
## job_titleBig Data Architect 35387.5 50230.8 0.704
## job_titleBig Data Engineer 35943.8 40416.2 0.889
## job_titleBusiness Data Analyst 12582.4 38397.0 0.328
## job_titleBusiness Intelligence Engineer 48214.1 44120.4 1.093
## job_titleCloud Data Architect 125996.5 60381.0 2.087
## job_titleCloud Data Engineer 76480.9 52497.7 1.457
## job_titleCloud Database Engineer 29450.6 42845.8 0.687
## job_titleCompliance Data Analyst -50691.7 65107.3 -0.779
## job_titleComputer Vision Engineer 64921.7 38888.3 1.669
## job_titleComputer Vision Software Engineer 26842.9 43325.5 0.620
## job_titleData Analyst 3884.2 37240.0 0.104
## job_titleData Analytics Consultant 6639.0 50256.7 0.132
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## job_titleData Architect 38281.2 37510.1 1.021
## job_titleData DevOps Engineer 28206.3 61108.3 0.462
## job_titleData Engineer 31148.9 37227.0 0.837
## job_titleData Infrastructure Engineer 66384.9 41922.4 1.584
## job_titleData Lead 86564.1 50084.1 1.728
## job_titleData Management Specialist 21752.6 66363.6 0.328
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## job_titleETL Developer 24152.4 40180.0 0.601
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## job_titleHead of Data Science 43696.3 40629.1 1.075
## job_titleHead of Machine Learning 5216.0 60415.3 0.086
## job_titleInsight Analyst -4535.3 50190.8 -0.090
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## job_titleLead Data Engineer 44445.3 43046.5 1.032
## job_titleLead Data Scientist 50986.1 40613.5 1.255
## job_titleLead Machine Learning Engineer 40963.3 46336.3 0.884
## job_titleMachine Learning Developer 22997.0 42379.8 0.543
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## job_titleML Engineer 55726.1 38128.4 1.462
## job_titleMLOps Engineer 28967.7 44106.7 0.657
## job_titleNLP Engineer 55047.1 41669.5 1.321
## job_titlePower BI Developer 20515.3 60222.7 0.341
## job_titlePrincipal Data Analyst 33831.1 50242.2 0.673
## job_titlePrincipal Data Architect 7178.5 60226.0 0.119
## job_titlePrincipal Data Engineer 67530.3 50133.6 1.347
## job_titlePrincipal Data Scientist 104638.2 40935.0 2.556
## job_titlePrincipal Machine Learning Engineer 65996.5 60381.0 1.093
## job_titleProduct Data Analyst 18114.0 43910.5 0.413
## job_titleProduct Data Scientist -16532.0 67549.2 -0.245
## job_titleResearch Engineer 62643.8 38020.0 1.648
## job_titleResearch Scientist 66899.8 37574.8 1.780
## job_titleSoftware Data Engineer 19364.5 53019.9 0.365
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## job_titleStaff Data Scientist -20935.9 60266.3 -0.347
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## Pr(>|t|)
## (Intercept) 0.546721
## experience_levelExecutive < 2e-16 ***
## experience_levelMid-Level 4.37e-09 ***
## experience_levelSenior-Level < 2e-16 ***
## job_titleAI Developer 0.001965 **
## job_titleAI Programmer 0.782797
## job_titleAI Scientist 0.191343
## job_titleAnalytics Engineer 0.364177
## job_titleApplied Data Scientist 0.093286 .
## job_titleApplied Machine Learning Engineer 0.440746
## job_titleApplied Machine Learning Scientist 0.415483
## job_titleApplied Scientist 0.063734 .
## job_titleAutonomous Vehicle Technician 0.538991
## job_titleAzure Data Engineer 0.481423
## job_titleBI Analyst 0.664516
## job_titleBI Data Analyst 0.915495
## job_titleBI Data Engineer 0.742118
## job_titleBI Developer 0.777622
## job_titleBig Data Architect 0.481169
## job_titleBig Data Engineer 0.373879
## job_titleBusiness Data Analyst 0.743163
## job_titleBusiness Intelligence Engineer 0.274561
## job_titleCloud Data Architect 0.036987 *
## job_titleCloud Data Engineer 0.145248
## job_titleCloud Database Engineer 0.491898
## job_titleCompliance Data Analyst 0.436274
## job_titleComputer Vision Engineer 0.095117 .
## job_titleComputer Vision Software Engineer 0.535584
## job_titleData Analyst 0.916937
## job_titleData Analytics Consultant 0.894912
## job_titleData Analytics Engineer 0.677622
## job_titleData Analytics Lead 0.004452 **
## job_titleData Analytics Manager 0.515905
## job_titleData Analytics Specialist 0.536827
## job_titleData Architect 0.307533
## job_titleData DevOps Engineer 0.644412
## job_titleData Engineer 0.402800
## job_titleData Infrastructure Engineer 0.113392
## job_titleData Lead 0.084007 .
## job_titleData Management Specialist 0.743097
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## job_titleData Operations Analyst 0.422753
## job_titleData Operations Engineer 0.766479
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## job_titleData Science Consultant 0.773753
## job_titleData Science Engineer 0.959135
## job_titleData Science Lead 0.171654
## job_titleData Science Manager 0.053800 .
## job_titleData Science Tech Lead 3.30e-05 ***
## job_titleData Scientist 0.330501
## job_titleData Scientist Lead 0.224949
## job_titleData Specialist 0.899796
## job_titleData Strategist 0.625966
## job_titleDeep Learning Engineer 0.443130
## job_titleDeep Learning Researcher 0.420088
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## job_titleETL Developer 0.547809
## job_titleETL Engineer 0.689226
## job_titleFinance Data Analyst 0.824822
## job_titleFinancial Data Analyst 0.834779
## job_titleHead of Data 0.050221 .
## job_titleHead of Data Science 0.282226
## job_titleHead of Machine Learning 0.931205
## job_titleInsight Analyst 0.928006
## job_titleLead Data Analyst 0.879092
## job_titleLead Data Engineer 0.301910
## job_titleLead Data Scientist 0.209416
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## job_titleMachine Learning Manager 0.421087
## job_titleMachine Learning Research Engineer 0.574331
## job_titleMachine Learning Researcher 0.454069
## job_titleMachine Learning Scientist 0.069322 .
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## job_titleMarketing Data Engineer 0.190230
## job_titleML Engineer 0.143955
## job_titleMLOps Engineer 0.511374
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## job_titleProduct Data Analyst 0.679983
## job_titleProduct Data Scientist 0.806672
## job_titleResearch Engineer 0.099511 .
## job_titleResearch Scientist 0.075088 .
## job_titleSoftware Data Engineer 0.714961
## job_titleStaff Data Analyst 0.031306 *
## job_titleStaff Data Scientist 0.728319
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## locationSG 0.229253
## locationSI 0.261232
## locationSK 0.091388 .
## locationTH 0.200266
## locationTR 0.046535 *
## locationUA 0.036384 *
## locationUS 0.076923 .
## locationVN 0.392033
## company_sizeM 0.490476
## company_sizeS 3.97e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 47400 on 3586 degrees of freedom
## Multiple R-squared: 0.4601, Adjusted R-squared: 0.4348
## F-statistic: 18.19 on 168 and 3586 DF, p-value: < 2.2e-16
Step 7: Refine the Model
refined_model <- lm(salary_usd ~ experience_level + job_title, data = data)
summary(refined_model)
##
## Call:
## lm(formula = salary_usd ~ experience_level + job_title, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -171618 -33761 -4253 31341 347577
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) -1532 26827 -0.057
## experience_levelExecutive 109830 6370 17.243
## experience_levelMid-Level 23641 3669 6.443
## experience_levelSenior-Level 67895 3363 20.189
## job_titleAI Developer 115383 31267 3.690
## job_titleAI Programmer 56532 46406 1.218
## job_titleAI Scientist 84670 29939 2.828
## job_titleAnalytics Engineer 88993 27346 3.254
## job_titleApplied Data Scientist 85433 31689 2.696
## job_titleApplied Machine Learning Engineer 77766 46426 1.675
## job_titleApplied Machine Learning Scientist 76532 30939 2.474
## job_titleApplied Scientist 130925 27726 4.722
## job_titleAutonomous Vehicle Technician 15989 46380 0.345
## job_titleAzure Data Engineer 33637 59901 0.562
## job_titleBI Analyst 63448 32194 1.971
## job_titleBI Data Analyst 39091 30142 1.297
## job_titleBI Data Engineer 61532 59895 1.027
## job_titleBI Developer 73928 30650 2.412
## job_titleBig Data Architect 59439 46414 1.281
## job_titleBig Data Engineer 43090 31271 1.378
## job_titleBusiness Data Analyst 50462 30142 1.674
## job_titleBusiness Intelligence Engineer 107787 37912 2.843
## job_titleCloud Data Architect 183637 59901 3.066
## job_titleCloud Data Engineer 35689 40933 0.872
## job_titleCloud Database Engineer 88637 35972 2.464
## job_titleCompliance Data Analyst 31532 59895 0.526
## job_titleComputer Vision Engineer 97178 29620 3.281
## job_titleComputer Vision Software Engineer 60919 35924 1.696
## job_titleData Analyst 61281 26895 2.278
## job_titleData Analytics Consultant 71211 46380 1.535
## job_titleData Analytics Engineer 38886 34571 1.125
## job_titleData Analytics Lead 167018 46406 3.599
## job_titleData Analytics Manager 87796 29158 3.011
## job_titleData Analytics Specialist 28637 46414 0.617
## job_titleData Architect 97587 27360 3.567
## job_titleData DevOps Engineer -12709 59901 -0.212
## job_titleData Engineer 87398 26869 3.253
## job_titleData Infrastructure Engineer 138191 34612 3.993
## job_titleData Lead 146137 46414 3.149
## job_titleData Management Specialist 1930 59901 0.032
## job_titleData Manager 62642 28600 2.190
## job_titleData Modeler 52537 46414 1.132
## job_titleData Operations Analyst 24199 37912 0.638
## job_titleData Operations Engineer 58674 31727 1.849
## job_titleData Quality Analyst 28647 33595 0.853
## job_titleData Science Consultant 47924 28930 1.657
## job_titleData Science Engineer 35970 35963 1.000
## job_titleData Science Lead 106566 32834 3.246
## job_titleData Science Manager 127404 27741 4.593
## job_titleData Science Tech Lead 308637 59901 5.152
## job_titleData Scientist 87158 26879 3.243
## job_titleData Scientist Lead 91917 46406 1.981
## job_titleData Specialist 70773 30394 2.329
## job_titleData Strategist 14637 46414 0.315
## job_titleDeep Learning Engineer 95012 34581 2.748
## job_titleDeep Learning Researcher 57042 59901 0.952
## job_titleDirector of Data Science 98279 31567 3.113
## job_titleETL Developer 73941 31727 2.331
## job_titleETL Engineer 49285 46426 1.062
## job_titleFinance Data Analyst -4467 59901 -0.075
## job_titleFinancial Data Analyst 87438 40912 2.137
## job_titleHead of Data 96759 31895 3.034
## job_titleHead of Data Science 71447 32504 2.198
## job_titleHead of Machine Learning -31989 60148 -0.532
## job_titleInsight Analyst 24677 46426 0.532
## job_titleLead Data Analyst 41609 35972 1.157
## job_titleLead Data Engineer 73254 34620 2.116
## job_titleLead Data Scientist 56247 32225 1.745
## job_titleLead Machine Learning Engineer 16564 40943 0.405
## job_titleMachine Learning Developer 62974 33572 1.876
## job_titleMachine Learning Engineer 102296 27000 3.789
## job_titleMachine Learning Infrastructure Engineer 92741 31310 2.962
## job_titleMachine Learning Manager 89338 40943 2.182
## job_titleMachine Learning Research Engineer 38441 37875 1.015
## job_titleMachine Learning Researcher 76388 34620 2.206
## job_titleMachine Learning Scientist 117577 28798 4.083
## job_titleMachine Learning Software Engineer 132846 31719 4.188
## job_titleManager Data Management 58637 59901 0.979
## job_titleMarketing Data Analyst 77964 46414 1.680
## job_titleMarketing Data Engineer 68502 59895 1.144
## job_titleML Engineer 110301 28344 3.891
## job_titleMLOps Engineer 106891 37926 2.818
## job_titleNLP Engineer 85388 33605 2.541
## job_titlePower BI Developer 6941 59895 0.116
## job_titlePrincipal Data Analyst 78264 46406 1.686
## job_titlePrincipal Data Architect -28209 59901 -0.471
## job_titlePrincipal Data Engineer 126137 46414 2.718
## job_titlePrincipal Data Scientist 132098 32845 4.022
## job_titlePrincipal Machine Learning Engineer 123637 59901 2.064
## job_titleProduct Data Analyst 39116 35952 1.088
## job_titleProduct Data Scientist -58363 59901 -0.974
## job_titleResearch Engineer 121075 28199 4.294
## job_titleResearch Scientist 114204 27451 4.160
## job_titleSoftware Data Engineer 18274 46406 0.394
## job_titleStaff Data Analyst -93298 60148 -1.551
## job_titleStaff Data Scientist 38637 59901 0.645
## Pr(>|t|)
## (Intercept) 0.954471
## experience_levelExecutive < 2e-16 ***
## experience_levelMid-Level 1.32e-10 ***
## experience_levelSenior-Level < 2e-16 ***
## job_titleAI Developer 0.000227 ***
## job_titleAI Programmer 0.223228
## job_titleAI Scientist 0.004708 **
## job_titleAnalytics Engineer 0.001147 **
## job_titleApplied Data Scientist 0.007049 **
## job_titleApplied Machine Learning Engineer 0.094004 .
## job_titleApplied Machine Learning Scientist 0.013419 *
## job_titleApplied Scientist 2.42e-06 ***
## job_titleAutonomous Vehicle Technician 0.730311
## job_titleAzure Data Engineer 0.574463
## job_titleBI Analyst 0.048822 *
## job_titleBI Data Analyst 0.194748
## job_titleBI Data Engineer 0.304331
## job_titleBI Developer 0.015914 *
## job_titleBig Data Architect 0.200404
## job_titleBig Data Engineer 0.168293
## job_titleBusiness Data Analyst 0.094184 .
## job_titleBusiness Intelligence Engineer 0.004493 **
## job_titleCloud Data Architect 0.002188 **
## job_titleCloud Data Engineer 0.383335
## job_titleCloud Database Engineer 0.013782 *
## job_titleCompliance Data Analyst 0.598605
## job_titleComputer Vision Engineer 0.001045 **
## job_titleComputer Vision Software Engineer 0.090016 .
## job_titleData Analyst 0.022754 *
## job_titleData Analytics Consultant 0.124771
## job_titleData Analytics Engineer 0.260745
## job_titleData Analytics Lead 0.000324 ***
## job_titleData Analytics Manager 0.002621 **
## job_titleData Analytics Specialist 0.537284
## job_titleData Architect 0.000366 ***
## job_titleData DevOps Engineer 0.831983
## job_titleData Engineer 0.001153 **
## job_titleData Infrastructure Engineer 6.66e-05 ***
## job_titleData Lead 0.001654 **
## job_titleData Management Specialist 0.974303
## job_titleData Manager 0.028567 *
## job_titleData Modeler 0.257744
## job_titleData Operations Analyst 0.523322
## job_titleData Operations Engineer 0.064491 .
## job_titleData Quality Analyst 0.393861
## job_titleData Science Consultant 0.097703 .
## job_titleData Science Engineer 0.317294
## job_titleData Science Lead 0.001183 **
## job_titleData Science Manager 4.52e-06 ***
## job_titleData Science Tech Lead 2.71e-07 ***
## job_titleData Scientist 0.001195 **
## job_titleData Scientist Lead 0.047699 *
## job_titleData Specialist 0.019938 *
## job_titleData Strategist 0.752514
## job_titleDeep Learning Engineer 0.006034 **
## job_titleDeep Learning Researcher 0.341022
## job_titleDirector of Data Science 0.001864 **
## job_titleETL Developer 0.019831 *
## job_titleETL Engineer 0.288487
## job_titleFinance Data Analyst 0.940554
## job_titleFinancial Data Analyst 0.032647 *
## job_titleHead of Data 0.002433 **
## job_titleHead of Data Science 0.028005 *
## job_titleHead of Machine Learning 0.594870
## job_titleInsight Analyst 0.595079
## job_titleLead Data Analyst 0.247468
## job_titleLead Data Engineer 0.034415 *
## job_titleLead Data Scientist 0.080989 .
## job_titleLead Machine Learning Engineer 0.685815
## job_titleMachine Learning Developer 0.060768 .
## job_titleMachine Learning Engineer 0.000154 ***
## job_titleMachine Learning Infrastructure Engineer 0.003076 **
## job_titleMachine Learning Manager 0.029171 *
## job_titleMachine Learning Research Engineer 0.310207
## job_titleMachine Learning Researcher 0.027414 *
## job_titleMachine Learning Scientist 4.54e-05 ***
## job_titleMachine Learning Software Engineer 2.88e-05 ***
## job_titleManager Data Management 0.327695
## job_titleMarketing Data Analyst 0.093092 .
## job_titleMarketing Data Engineer 0.252821
## job_titleML Engineer 0.000101 ***
## job_titleMLOps Engineer 0.004853 **
## job_titleNLP Engineer 0.011097 *
## job_titlePower BI Developer 0.907752
## job_titlePrincipal Data Analyst 0.091786 .
## job_titlePrincipal Data Architect 0.637715
## job_titlePrincipal Data Engineer 0.006606 **
## job_titlePrincipal Data Scientist 5.89e-05 ***
## job_titlePrincipal Machine Learning Engineer 0.039085 *
## job_titleProduct Data Analyst 0.276656
## job_titleProduct Data Scientist 0.329954
## job_titleResearch Engineer 1.80e-05 ***
## job_titleResearch Scientist 3.25e-05 ***
## job_titleSoftware Data Engineer 0.693767
## job_titleStaff Data Analyst 0.120954
## job_titleStaff Data Scientist 0.518960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 53550 on 3659 degrees of freedom
## Multiple R-squared: 0.297, Adjusted R-squared: 0.2788
## F-statistic: 16.27 on 95 and 3659 DF, p-value: < 2.2e-16
Interpretation: Location and company size were insignificant predictors and removed from the refined model. Experience level and job title remain significant, explaining most of the salary variation.
Step 8: Predictions with Confidence and Prediction Intervals
new_data <- data.frame(
experience_level = "Senior-Level",
job_title = "Data Scientist"
)
confidence_interval <- predict(refined_model, newdata = new_data, interval = "confidence")
prediction_interval <- predict(refined_model, newdata = new_data, interval = "prediction")
print(confidence_interval)
## fit lwr upr
## 1 153521.6 149745.5 157297.7
print(prediction_interval)
## fit lwr upr
## 1 153521.6 48461.75 258581.4
Interpretation: Confidence Interval: Expected salary for a Senior-Level Data Scientist ranges from $149,745 to $157,298.
Prediction Interval: Individual salaries for this role could vary widely, from $48,462 to $258,581.