Objective
The goal of this case is to identify the target market for each of the five automobile models under consideration by analyzing differences in desirability ratings across demographic groups. This analysis will guide Auto Concepts in developing focused marketing strategies tailored to each model’s potential buyers.
Deliverables
Difference Analysis: Conduct the relevant difference analysis for each automobile type by each demographic variable. If necessary, conduct post-hoc tests.
Target Market Profiles: Identify the most desirable demographic groups for each model.
R Code and Results
Include well-documented R scripts used for the analysis (Chi-Square tests, correlations, etc.).
Present outputs of the analysis, such as tables, graphs, and statistical test results.
Ensure reproducibility by providing clear instructions or annotations within the R code.
Solutions
1. Super Cycle
1.1 Difference Analysis
# Load necessary librarieslibrary(tidyverse)library(knitr)library(kableExtra)# Load datasetsetwd("~/Documents/GitHub/Marketing-Research-2025-Spring")auto_concept <-read_csv("auto_concept.csv")#Analysis for supercycle1seat----------------# T-test for "Super Cycle" by Gendert_test_gender <-t.test(supercycle1seat ~ gender, data = auto_concept)print(t_test_gender)
Welch Two Sample t-test
data: supercycle1seat by gender
t = 13.922, df = 982.09, p-value < 2.2e-16
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
0.848863 1.127436
sample estimates:
mean in group 0 mean in group 1
3.076786 2.088636
# Result: Males have significantly higher desirability ratings for "Super Cycle."# T-test for "Super Cycle" by Marital Statust_test_marital <-t.test(supercycle1seat ~ marital, data = auto_concept)print(t_test_marital)
Welch Two Sample t-test
data: supercycle1seat by marital
t = 4.5988, df = 121.53, p-value = 1.05e-05
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
0.4326959 1.0868138
sample estimates:
mean in group 0 mean in group 1
3.318182 2.558427
# Result: Unmarried individuals have significantly higher desirability ratings for "Super Cycle."# ANOVA for "Super Cycle" by Ageanova_age <-aov(supercycle1seat ~factor(age), data = auto_concept)summary(anova_age)
Df Sum Sq Mean Sq F value Pr(>F)
factor(age) 4 355.8 88.96 76.44 <2e-16 ***
Residuals 995 1158.0 1.16
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Agetukey_age <-TukeyHSD(anova_age)print(tukey_age)
# Result: 20-29 age group has the highest desirability for "Super Cycle."# ANOVA for "Super Cycle" by Hometown Sizeanova_town <-aov(supercycle1seat ~factor(townsize), data = auto_concept)summary(anova_town)
Df Sum Sq Mean Sq F value Pr(>F)
factor(townsize) 4 103 25.759 18.17 2.05e-14 ***
Residuals 995 1411 1.418
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Hometown Sizetukey_town <-TukeyHSD(anova_town)print(tukey_town)
# Result: Individuals from hometowns with 1 million and more population have the highest desirability.# ANOVA for "Super Cycle" by Income Levelanova_income <-aov(supercycle1seat ~factor(income), data = auto_concept)summary(anova_income)
Df Sum Sq Mean Sq F value Pr(>F)
factor(income) 4 186.8 46.70 35.02 <2e-16 ***
Residuals 995 1327.0 1.33
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Incometukey_income <-TukeyHSD(anova_income)print(tukey_income)
# Result: Individuals with income under $50K have the highest desirability for "Super Cycle."# ANOVA for "Super Cycle" by Education Levelanova_education <-aov(supercycle1seat ~factor(edcation), data = auto_concept)summary(anova_education)
Df Sum Sq Mean Sq F value Pr(>F)
factor(edcation) 4 231.2 57.81 44.85 <2e-16 ***
Residuals 995 1282.6 1.29
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Education Leveltukey_education <-TukeyHSD(anova_education)print(tukey_education)
# Result: Individuals with less than high school education have the highest desirability for "Super Cycle."
1.2 Target Market Profiles
Target Market Profile for Super Cycle - 1 Seat All-Electric Motorcycle
Demographic
Segment
Gender
Males
Marital Status
Unmarried
Hometown Size
1 million and more
Age Group
20-29 years old
Education Level
Less than high school
Income Level
Under $50K
2. Runabout Sport
2.1 Difference Analysis
#Analysis for runaboutsport2seat----------------# T-test for "Runabout Sport" by Gendert_test_gender <-t.test(runaboutsport2seat ~ gender, data = auto_concept)print(t_test_gender)
Welch Two Sample t-test
data: runaboutsport2seat by gender
t = -0.49153, df = 969.6, p-value = 0.6232
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.2382751 0.1428205
sample estimates:
mean in group 0 mean in group 1
3.900000 3.947727
# Result: Both Males and Females have similar desirability, no strong gender preference.# T-test for "Runabout Sport" by Marital Statust_test_marital <-t.test(runaboutsport2seat ~ marital, data = auto_concept)print(t_test_marital)
Welch Two Sample t-test
data: runaboutsport2seat by marital
t = -2.3079, df = 122.26, p-value = 0.02269
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.87877336 -0.06729406
sample estimates:
mean in group 0 mean in group 1
3.500000 3.973034
# Result: Married individuals have significantly higher desirability ratings for "Runabout Sport."# ANOVA for "Runabout Sport" by Ageanova_age <-aov(runaboutsport2seat ~factor(age), data = auto_concept)summary(anova_age)
Df Sum Sq Mean Sq F value Pr(>F)
factor(age) 4 388 97.01 48.98 <2e-16 ***
Residuals 995 1971 1.98
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Agetukey_age <-TukeyHSD(anova_age)print(tukey_age)
# Result: 30-49 years old group has the highest desirability for "Runabout Sport."# ANOVA for "Runabout Sport" by Hometown Sizeanova_town <-aov(runaboutsport2seat ~factor(townsize), data = auto_concept)summary(anova_town)
Df Sum Sq Mean Sq F value Pr(>F)
factor(townsize) 4 520.6 130.16 70.46 <2e-16 ***
Residuals 995 1838.1 1.85
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Hometown Sizetukey_town <-TukeyHSD(anova_town)print(tukey_town)
# Result: Individuals from hometowns of 500K to 1 million population have the highest desirability.# ANOVA for "Runabout Sport" by Income Levelanova_income <-aov(runaboutsport2seat ~factor(income), data = auto_concept)summary(anova_income)
Df Sum Sq Mean Sq F value Pr(>F)
factor(income) 4 578.6 144.65 80.85 <2e-16 ***
Residuals 995 1780.2 1.79
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Incometukey_income <-TukeyHSD(anova_income)print(tukey_income)
# Result: Individuals with income between $50K and $75K have the highest desirability for "Runabout Sport."# ANOVA for "Runabout Sport" by Education Levelanova_education <-aov(runaboutsport2seat ~factor(edcation), data = auto_concept)summary(anova_education)
Df Sum Sq Mean Sq F value Pr(>F)
factor(edcation) 4 871 217.8 145.6 <2e-16 ***
Residuals 995 1488 1.5
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Education Leveltukey_education <-TukeyHSD(anova_education)print(tukey_education)
# Result: Individuals with some college education have the highest desirability for "Runabout Sport."
2.2 Target Market Profiles
Target Market Profile for Runabout Sport - 2 Seat All-Electric Sports Car
Demographic
Segment
Gender
Both Males and Females
Marital Status
Married
Hometown Size
500K to 1 million
Age Group
30-49 years old
Education Level
Some college
Income Level
$50K-$75K
3. Runabout with Stowage
3.1 Difference Analysis
#Analysis for runaboutstowage2seat----------------# T-test for "Runabout with Stowage" by Gendert_test_gender <-t.test(runaboutstowage2seat ~ gender, data = auto_concept)print(t_test_gender)
Welch Two Sample t-test
data: runaboutstowage2seat by gender
t = -0.506, df = 886.14, p-value = 0.613
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.3049197 0.1799197
sample estimates:
mean in group 0 mean in group 1
3.9375 4.0000
# Result: Both Males and Females have similar desirability ratings for "Runabout with Stowage."# T-test for "Runabout with Stowage" by Marital Statust_test_marital <-t.test(runaboutstowage2seat ~ marital, data = auto_concept)print(t_test_marital)
Welch Two Sample t-test
data: runaboutstowage2seat by marital
t = -4.7723, df = 137.47, p-value = 4.598e-06
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-1.2879375 -0.5333087
sample estimates:
mean in group 0 mean in group 1
3.154545 4.065169
# Result: Married individuals have significantly higher desirability ratings for "Runabout with Stowage."# ANOVA for "Runabout with Stowage" by Ageanova_age <-aov(runaboutstowage2seat ~factor(age), data = auto_concept)summary(anova_age)
Df Sum Sq Mean Sq F value Pr(>F)
factor(age) 4 478 119.52 37.5 <2e-16 ***
Residuals 995 3172 3.19
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Agetukey_age <-TukeyHSD(anova_age)print(tukey_age)
# Result: 30-49 years old group has the highest desirability for "Runabout with Stowage."# ANOVA for "Runabout with Stowage" by Hometown Sizeanova_town <-aov(runaboutstowage2seat ~factor(townsize), data = auto_concept)summary(anova_town)
Df Sum Sq Mean Sq F value Pr(>F)
factor(townsize) 4 390 97.40 29.73 <2e-16 ***
Residuals 995 3260 3.28
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Hometown Sizetukey_town <-TukeyHSD(anova_town)print(tukey_town)
# Result: Individuals from hometowns of 10K to 500K population have the highest desirability.# ANOVA for "Runabout with Stowage" by Income Levelanova_income <-aov(runaboutstowage2seat ~factor(income), data = auto_concept)summary(anova_income)
Df Sum Sq Mean Sq F value Pr(>F)
factor(income) 4 743.6 185.91 63.65 <2e-16 ***
Residuals 995 2906.1 2.92
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Incometukey_income <-TukeyHSD(anova_income)print(tukey_income)
# Result: Individuals with income between $76K and $150K have the highest desirability for "Runabout with Stowage."# ANOVA for "Runabout with Stowage" by Education Levelanova_education <-aov(runaboutstowage2seat ~factor(edcation), data = auto_concept)summary(anova_education)
Df Sum Sq Mean Sq F value Pr(>F)
factor(edcation) 4 1575 393.7 188.8 <2e-16 ***
Residuals 995 2075 2.1
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Education Leveltukey_education <-TukeyHSD(anova_education)print(tukey_education)
# Result: Individuals with a college degree have the highest desirability for "Runabout with Stowage."
3.2 Target Market Profiles
Target Market Profile for Runabout with Stowage - 2 Seat Hybrid
Demographic
Segment
Gender
Both Males and Females
Marital Status
Married
Hometown Size
10K to 500K
Age Group
30-49 years old
Education Level
College degree
Income Level
$76K-$150K
4. Economy Hybrid
4.1 Difference Analysis
#Analysis for economyhybrid4seat----------------# T-test for "Economy Hybrid" by Gendert_test_gender <-t.test(economyhybrid4seat ~ gender, data = auto_concept)print(t_test_gender)
Welch Two Sample t-test
data: economyhybrid4seat by gender
t = 1.3349, df = 865.24, p-value = 0.1823
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.07199827 0.37816710
sample estimates:
mean in group 0 mean in group 1
3.530357 3.377273
# Result: Males and Females both show significant interest, no strong gender differentiation.# T-test for "Economy Hybrid" by Marital Statust_test_marital <-t.test(economyhybrid4seat ~ marital, data = auto_concept)print(t_test_marital)
Welch Two Sample t-test
data: economyhybrid4seat by marital
t = -4.211, df = 142.24, p-value = 4.488e-05
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-1.0346077 -0.3735639
sample estimates:
mean in group 0 mean in group 1
2.836364 3.540449
# Result: Married individuals show significantly higher desirability.# ANOVA for "Economy Hybrid" by Ageanova_age <-aov(economyhybrid4seat ~factor(age), data = auto_concept)summary(anova_age)
Df Sum Sq Mean Sq F value Pr(>F)
factor(age) 4 860.7 215.16 94.65 <2e-16 ***
Residuals 995 2262.0 2.27
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Agetukey_age <-TukeyHSD(anova_age)print(tukey_age)
# Result: The 50-59 age group shows the highest desirability.# ANOVA for "Economy Hybrid" by Hometown Sizeanova_town <-aov(economyhybrid4seat ~factor(townsize), data = auto_concept)summary(anova_town)
Df Sum Sq Mean Sq F value Pr(>F)
factor(townsize) 4 180.5 45.14 15.27 4.03e-12 ***
Residuals 995 2942.1 2.96
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Hometown Sizetukey_town <-TukeyHSD(anova_town)print(tukey_town)
# Result: 100K to 500K and potentially 1 million or more population sizes show the highest desirability.# ANOVA for "Economy Hybrid" by Income Levelanova_income <-aov(economyhybrid4seat ~factor(income), data = auto_concept)summary(anova_income)
Df Sum Sq Mean Sq F value Pr(>F)
factor(income) 4 1254 313.63 167 <2e-16 ***
Residuals 995 1868 1.88
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Incometukey_income <-TukeyHSD(anova_income)print(tukey_income)
# Result: Over $150K income group shows the highest desirability.# ANOVA for "Economy Hybrid" by Education Levelanova_education <-aov(economyhybrid4seat ~factor(edcation), data = auto_concept)summary(anova_education)
Df Sum Sq Mean Sq F value Pr(>F)
factor(edcation) 4 996 249.00 116.5 <2e-16 ***
Residuals 995 2127 2.14
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Education Leveltukey_education <-TukeyHSD(anova_education)print(tukey_education)
# Result: Graduate or professional degree holders show the highest desirability.
4.2 Target Market Profiles
Target Market Profile for Economy Hybrid - 4 Seat Hybrid
Demographic
Segment
Gender
Both Males and Females
Marital Status
Married
Hometown Size
100K to 500K
Age Group
50-59 years old
Education Level
Graduate or professional degree
Income Level
Over $150K
5. Economy Gasoline
5.1 Difference Analysis
#Analysis for economyhybrid4seat----------------# T-test for "Economy Gasoline" by Gendert_test_gender <-t.test(economygas4seat ~ gender, data = auto_concept)print(t_test_gender)
Welch Two Sample t-test
data: economygas4seat by gender
t = 7.7329, df = 997.49, p-value = 2.562e-14
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
0.5039503 0.8466990
sample estimates:
mean in group 0 mean in group 1
3.507143 2.831818
# Result: Males show slightly higher desirability ratings for "Economy Gasoline."# T-test for "Economy Gasoline" by Marital Statust_test_marital <-t.test(economygas4seat ~ marital, data = auto_concept)print(t_test_marital)
Welch Two Sample t-test
data: economygas4seat by marital
t = -3.9866, df = 139.92, p-value = 0.0001075
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-0.8419347 -0.2837037
sample estimates:
mean in group 0 mean in group 1
2.709091 3.271910
# Result: Married individuals show significantly higher desirability ratings.# ANOVA for "Economy Gasoline" by Ageanova_age <-aov(economygas4seat ~factor(age), data = auto_concept)summary(anova_age)
Df Sum Sq Mean Sq F value Pr(>F)
factor(age) 4 415.8 104.0 61.13 <2e-16 ***
Residuals 995 1692.1 1.7
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Agetukey_age <-TukeyHSD(anova_age)print(tukey_age)
# Result: The 60+ age group shows the highest desirability for "Economy Gasoline."# ANOVA for "Economy Gasoline" by Hometown Sizeanova_town <-aov(economygas4seat ~factor(townsize), data = auto_concept)summary(anova_town)
Df Sum Sq Mean Sq F value Pr(>F)
factor(townsize) 4 524.3 131.06 82.35 <2e-16 ***
Residuals 995 1583.6 1.59
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Hometown Sizetukey_town <-TukeyHSD(anova_town)print(tukey_town)
# Result: Under 10K ('5') is the most desirable hometown size# ANOVA for "Economy Gasoline" by Income Levelanova_income <-aov(economygas4seat ~factor(income), data = auto_concept)summary(anova_income)
Df Sum Sq Mean Sq F value Pr(>F)
factor(income) 4 46.7 11.687 5.642 0.000172 ***
Residuals 995 2061.2 2.072
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Incometukey_income <-TukeyHSD(anova_income)print(tukey_income)
# Result: Individuals with income levels 150K over show the highest desirability.# ANOVA for "Economy Gasoline" by Education Levelanova_education <-aov(economygas4seat ~factor(edcation), data = auto_concept)summary(anova_education)
Df Sum Sq Mean Sq F value Pr(>F)
factor(edcation) 4 121 30.258 15.15 4.95e-12 ***
Residuals 995 1987 1.997
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Perform Tukey post-hoc analysis for Education Leveltukey_education <-TukeyHSD(anova_education)print(tukey_education)