E = row total × column total / grand total, df = (r − 1)(c − 1)
Critical values (α = 0.05)
df 1: 3.841, 2: 5.991, 3: 7.815, 4: 9.488
Decision
reject H0 if χ² > χ²crit
2Practice problems
1.Normal distributionMedium
The IQ scores are normally distributed with mean 100 points and standard deviation 15 points. What fraction lies between 70.75 and 122.5 points? Give P as a decimal.
μ = 100σ = 15x1 = 70.75x2 = 122.5
P =
2.Normal distributionHard
The heights of adult men are normally distributed with mean 178 cm and standard deviation 7 cm. Drag the cut-off so the shaded top 30% is to the right of it. Find the cut-off x.
μ = 178 cmσ = 7 cm30%
x = cm
3.Confidence intervalsMedium
The blood pressure of 15 patients is measured before and after a treatment. The differences d = before − after (mmHg) have mean 5.6 and standard deviation 7.8. Find the lower limit of the 95% confidence interval for the mean difference.
n = 15d̄ = 5.6sd = 7.895%t(14) = 2.145
lower limit = mmHg
4.Confidence intervalsHard
Two fertilisers are tested. Plot group 1: 7 plots, mean yield 66.5 kg, standard deviation 8.5 kg. Plot group 2: 14 plots, mean 78 kg, standard deviation 10 kg. Assume equal variances (pooled t, df = n₁ + n₂ − 2). Find the lower limit of the 99% confidence interval for μ₁ − μ₂.
A sample of 100 students has a mean score of 197.9 and a sample standard deviation of 15. Find the test statistic t for H₀: μ = 200.
μ₀ = 200x̄ = 197.9s = 15n = 100
t =
6.Hypothesis tests & confidence intervalsHard
How large must a sample be so that the 99% confidence interval for a mean has a margin of error of at most 3? The standard deviation is 10. Find the smallest sample size n.
σ = 10E = 399%
n =
7.Linear regression & correlationMedium
The table gives 6 paired observations of x = hours studied and y = exam score. Find the coefficient of determination r² of the least-squares line.
n = 6
r² =
8.Linear regression & correlationHard
The table gives 6 paired observations of x = hours studied and y = exam score. Use the least-squares line ŷ = a + b·x. A new observation (4, 58) is made. Find its residual e = y − ŷ.
n = 6(4, 58)
e =
9.Chi-square testsMedium
The contingency table shows observed counts. Compute the chi-square statistic χ² for the test of independence between the row and column variables. Do not use a continuity correction.
N = 1402 × 2
χ² =
10.Chi-square testsHard
The table shows observed counts. Test at α = 0.01 whether the two variables are independent (no continuity correction). Compute the test statistic χ², then compare it with the critical value from the table.
N = 1803 × 2α = 0.01
χ² =
3Solutions
1.Normal distribution
Answer: P = 0.9076
Normal distribution: convert a value x to a z-score, z = (x − μ)/σ, which counts standard deviations from the mean. A z-table (or Φ(z)) gives the area to the left of z, which is P(X < x). The area to the right is 1 − Φ(z), and the area between two values is the difference of their two areas.
z-scores
z₁ = (70.75 − 100)/15 = −1.95
z₂ = (122.5 − 100)/15 = 1.5
Area between
P = Φ(1.5) − Φ(−1.95) = 0.9332 − 0.02559 = 0.9076
Answer
P = 0.9076
2.Normal distribution
Answer: x = 181.6 cm
From an area back to z: Φ(z) is the area to the left of z. For a left-tail area A, find z with Φ(z) = A in the table. A right-tail area A means Φ(z) = 1 − A. A central area A between −z and z leaves (1 − A)/2 in each tail, so Φ(z) = (1 + A)/2. For a value x, use x = μ + zσ.
Area to the left
Φ(z) = 0.7
z from the table
z ≈ 0.52
Back to the original units
x = 178 + 0.52·7 = 181.6 cm
Answer
x = 181.6 cm
3.Confidence intervals
Answer: lower limit = 1.28 mmHg
Interval for a difference: estimate ± critical value × standard error. For two independent means with known σ: SE = √(σ₁²/n₁ + σ₂²/n₂) and z. With equal unknown variances: pooled s_p and t with n₁ + n₂ − 2 degrees of freedom. For paired data, work with the differences: d̄ ± t·s_d/√n.
Critical value
df = 14, t = 2.145
Margin of error
E = 2.145·7.8/√15 = 4.32
Interval
5.6 ± 4.32 → [1.28, 9.92]
Answer
lower limit = 1.28 mmHg
4.Confidence intervals
Answer: lower limit = −24.15 kg
Interval for a difference: estimate ± critical value × standard error. For two independent means with known σ: SE = √(σ₁²/n₁ + σ₂²/n₂) and z. With equal unknown variances: pooled s_p and t with n₁ + n₂ − 2 degrees of freedom. For paired data, work with the differences: d̄ ± t·s_d/√n.
Pooled standard deviation
sp² = (6·8.5² + 13·10²)/19 = 91.24
sp = 9.552
Margin of error
E = 2.861·9.552·√(1/7 + 1/14) = 12.65
Interval
−11.5 ± 12.65 → [−24.15, 1.15]
Answer
lower limit = −24.15 kg
5.Hypothesis tests & confidence intervals
Answer: t = −1.4
Testing a mean: the test statistic measures how many standard errors the sample mean lies from the value claimed in H₀: z = (x̄ − μ₀)/(σ/√n) when σ is known, t = (x̄ − μ₀)/(s/√n) when it is estimated from the sample. A confidence interval is x̄ ± (critical value)·(standard error).
Standard error
SE = 15/√100 = 15/10 = 1.5
Test statistic
t = (197.9 − 200)/1.5 = −1.4
Answer
t = −1.4
t0.025; df = 99 = 1.984
|t| < 1.984, so H₀ is not rejected at the 5% level (two-sided).
6.Hypothesis tests & confidence intervals
Answer: n = 74
Testing a mean: the test statistic measures how many standard errors the sample mean lies from the value claimed in H₀: z = (x̄ − μ₀)/(σ/√n) when σ is known, t = (x̄ − μ₀)/(s/√n) when it is estimated from the sample. A confidence interval is x̄ ± (critical value)·(standard error).
Solve for n
n = (2.576·10/3)² = 73.73
Round up
n = 74
Answer
n = 74
7.Linear regression & correlation
Answer: r² = 0.9854
Least-squares regression: with x̄ and ȳ the means, Sxx = Σ(x − x̄)², Syy = Σ(y − ȳ)² and Sxy = Σ(x − x̄)(y − ȳ), the line ŷ = a + b·x has slope b = Sxy/Sxx and intercept a = ȳ − b·x̄, so it always passes through (x̄, ȳ). Pearson's correlation coefficient r = Sxy/√(Sxx·Syy) lies between −1 and 1; r² is the fraction of the variation in y explained by the line.
Means
x̄ = 36/6 = 6
ȳ = 425/6 = 70.83
Sums of squares and products
Sxx = Σ(x − x̄)² = 70
Sxy = Σ(x − x̄)(y − ȳ) = 397
Syy = Σ(y − ȳ)² = 2285
Coefficient of determination
r² = 397²/(70·2285) = 0.9854
Answer
r² = 0.9854
About 98.54% of the variation in y is explained by the line.
8.Linear regression & correlation
Answer: e = 4.167
Least-squares regression: with x̄ and ȳ the means, Sxx = Σ(x − x̄)², Syy = Σ(y − ȳ)² and Sxy = Σ(x − x̄)(y − ȳ), the line ŷ = a + b·x has slope b = Sxy/Sxx and intercept a = ȳ − b·x̄, so it always passes through (x̄, ȳ). Pearson's correlation coefficient r = Sxy/√(Sxx·Syy) lies between −1 and 1; r² is the fraction of the variation in y explained by the line.
Means
x̄ = 48/6 = 8
ȳ = 407/6 = 67.83
Sums of squares and products
Sxx = Σ(x − x̄)² = 70
Sxy = Σ(x − x̄)(y − ȳ) = 245
Regression line
b = 245/70 = 3.5
a = 67.83 − 3.5·8 = 39.83
ŷ = 39.83 + 3.5·x
Predicted value
ŷ = 39.83 + 3.5·4 = 53.83
Residual
e = 58 − 53.83 = 4.167
Answer
e = 4.167
The point lies above the line.
9.Chi-square tests
Answer: χ² = 14.58
Chi-square tests: compare observed counts O with the counts E expected under H₀ using χ² = Σ(O − E)²/E. Goodness of fit: E = n·p for each category, df = k − 1 (minus one more for each parameter estimated from the data). Independence in an r × c table: E = row total × column total / grand total, df = (r − 1)(c − 1). Reject H₀ when χ² exceeds the critical value for the chosen α.
df = 1. At α = 0.05 the critical value is 3.841, so H₀ (independence) is rejected.
10.Chi-square tests
Answer: χ² = 6.86
Chi-square tests: compare observed counts O with the counts E expected under H₀ using χ² = Σ(O − E)²/E. Goodness of fit: E = n·p for each category, df = k − 1 (minus one more for each parameter estimated from the data). Independence in an r × c table: E = row total × column total / grand total, df = (r − 1)(c − 1). Reject H₀ when χ² exceeds the critical value for the chosen α.