# data[].attributes.body
Step 1: Each group gets a different dataset (e.g., hours studied vs

marks, temperature vs hot drink sales, etc.).

Step 2:

Draw a quick scatter plot (rough sketch is fine).

Decide if the relationship is linear or non-linear.

Discuss if there may be outliers.

Step 3:

Predict whether Pearson’s r will be high, low, positive, or negative.

Decide: Would regression be suitable here? Why or why not?

# data[].attributes.body
🌡️ Dataset 2: Temperature vs Hot Drink Sales

# data[].attributes.body
•Given a small dataset with totals already computed, Use the formula to calculate r yourself.

•Interpret the result (direction + strength). State whether regression is suitable and why.

# data[].attributes.body
•Given several pairs of variables, decide. Is the likely correlation positive, negative, or zero?

•Is regression likely useful or not useful? (Explain in 1–2 sentences each.)

# data[].attributes.body
1

# data[].attributes.body
Answer these questions in your notebook:

•Q1: What do you think correlation means?

•Q2: What do you think regression is used for?

•Q3: Which of the following is the correct range of Pearson’s r?

a) 0 to 1

b) −1 to +1

c) −10 to +10

•Q4: If r = 0, what does that suggest about the relationship between two variables?

# data[].attributes.body
•Look at these pairs of variables and decide:

•Is the relationship likely positive, negative, or no correlation?

•Is it likely to be strong or weak?

1.Hours studied and exam marks

2.Age and number of toys owned

3.Temperature and sales of ice cream

4.Height and weight

# data[].attributes.body
•Task:

•You are given a small dataset (e.g., height and weight).

•Individually, calculate:

•∑x,∑y,∑x y,∑x^2 ,∑y^2

•Use the formula to compute r.

•State:

•The direction (positive / negative / none).

•The strength (weak / moderate / strong).

# data[].attributes.body
•Given a dataset with one outlier:

•Calculate r with the outlier. Remove the outlier and calculate r again.

•Compare the values and write a short paragraph on:

•How outliers affect r

•How they affect the decision to use regression.

# data[].attributes.body
1.Define Pearson’s r in one sentence.

2.What are two assumptions required to use Pearson’s r

correctly?

3.If r=−0.8, what does that tell you about the relationship

between x and y?

4.Give one situation where regression may not be suitable.

# data[].attributes.body
•Q1:  On a scale of 1–5, how confident do I feel about:Understanding what Pearson’s r means?Interpreting the value of r?Knowing when regression is suitable or not?

•Q2:  One thing I understood well today was…

•Q3:  One thing I am still unsure about is…

•Q4:  One question I would like to ask about correlation or regression is…

# data[].attributes.body
🚶 Dataset 3: Age vs Daily Steps

# data[].attributes.headline
Group 5 : 💧 Dataset 5: Water Intake vs Skin Hydration Score

# data[].attributes.subject
Group 5 : 💧 Dataset 5: Water Intake vs Skin Hydration Score

# data[].attributes.headline
Group 6 : 🚗 Dataset 6: Car Age vs Resale Value

# data[].attributes.subject
Group 6 : 🚗 Dataset 6: Car Age vs Resale Value

# data[].attributes.headline
Group 7 : 🏃 Dataset 7: Exercise Time vs Calories Burned

# data[].attributes.subject
Group 7 : 🏃 Dataset 7: Exercise Time vs Calories Burned

# data[].attributes.body
1)strong positive 2) strong negative 3)strong positive 4) weak positive

# data[].attributes.body
•Explore other correlation measures:

•Spearman’s rank correlation for ordinal or non-linear relationships.

•Find a real dataset online (e.g., from sports, economics, or health):

•Identify possible pairs of variables.

•Predict whether the correlation is positive/negative/weak/strong.

•Investigate:

•How correlation is used in finance, medicine, or social science research.

•Prepare a short note or slide on one real-life example.