Inferential Statistics & Hypothesis Testing

by Posted on

Inferential statistics help draw conclusions from data using probability-based tests.

1. Correlation Analysis (Checking Relationships)

  • Pearson’s Correlation (For Continuous Data):
    r
    cor(data$Age, data$Salary) # R
    data[['Age', 'Salary']].corr() # Python
  • Spearman’s Correlation (For Ranked Data)
    r
    cor(data$Rank1, data$Rank2, method="spearman") # R

2. T-Test (Comparing Two Groups)

  • SPSS: Analyze > Compare Means > Independent-Samples T Test
  • R Example:
    r
    t.test(Salary ~ Gender, data = data)
  • Python Example:
    python
    from scipy.stats import ttest_ind
    ttest_ind(data['Salary'][data['Gender']=='Male'], data['Salary'][data['Gender']=='Female'])

3. ANOVA (Comparing More Than Two Groups)

  • SPSS: Analyze > Compare Means > One-Way ANOVA
  • R Example:
    r
    anova_model <- aov(Salary ~ Department, data = data)
    summary(anova_model)
  • Python Example:
    python
    from scipy.stats import f_oneway
    f_oneway(data['Salary'][data['Department']=='HR'], data['Salary'][data['Department']=='IT'])

✅ Tip: Use ANOVA for 3+ group comparisons and T-Test for 2-group comparisons.

📌 Step 5: Regression Analysis (Predictive Modeling)

Regression helps predict relationships between variables.

1. Linear Regression (Predicting a Continuous Variable)

  • R Example:
    r
    model <- lm(Salary ~ Age + Experience, data = data)
    summary(model)
  • Python Example:
    python
    from sklearn.linear_model import LinearRegression
    model = LinearRegression()
    X = data[['Age', 'Experience']]
    y = data['Salary']
    model.fit(X, y)
    print(model.coef_)

2. Logistic Regression (Predicting Binary Outcomes)

  • R Example:
    r
    model <- glm(Promotion ~ Age + Experience, data = data, family = "binomial")
    summary(model)
  • Python Example:
    python
    from sklearn.linear_model import LogisticRegression
    model = LogisticRegression()
    model.fit(X, y)

✅ Tip: Use linear regression for continuous outcomes and logistic regression for categorical outcomes.

📌 Step 6: Data Visualization

1. Using Tableau for Dashboards

  • Import data into Tableau
  • Drag & drop fields to create bar charts, scatter plots, heatmaps
  • Use filters for interactive dashboards

2. Visualizing in R (ggplot2)

r
library(ggplot2)
ggplot(data, aes(x=Age, y=Salary)) + geom_point() + geom_smooth(method="lm")

3. Visualizing in Python (Matplotlib & Seaborn)

python
import seaborn as sns
sns.scatterplot(x="Age", y="Salary", data=data)

✅ Tip: Data visualization helps in interpreting patterns and communicating findings effectively.

📌 Summary: Quantitative Data Analysis Workflow

✅ Import & Clean Data → ✅ Descriptive Statistics → ✅ Inferential Tests (T-Test, ANOVA, Regression) → ✅ Visualize & Interpret Results

🚀 Need help with data analysis? Our experts at StatisticsProjectHelper.com specialize in SPSS, R, Python, Stata, and Tableau for quantitative data analysis, predictive modeling, and research projects. Contact us today!

Published by Henry