Statistics 2: Statistical Inference: cram pack

Formula sheets, the key method steps for every topic and 10 practice problems, with full solutions at the back.

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1Formulas and methods

Normal distribution

z-score
z = (x − μ)/σ
Below x
P(X < x) = Φ(z)
Above x
P(X > x) = 1 − Φ(z)
Between
Φ(z2) − Φ(z1)
Percentile
x = μ + z·σ

Method

  1. Convert the value to a z-score: z = (x − μ)/σ.
  2. Look up Φ(z), the area to the left of z.
  3. For the area to the right use 1 − Φ(z); for an interval subtract the two left areas.
  4. For a percentile, work backwards: find z for the area, then x = μ + z·σ.

Confidence intervals

σ known
x̄ ± z·σ/√n
σ unknown
x̄ ± t·s/√n, df = n − 1
Critical z
90%: 1.645, 95%: 1.96, 99%: 2.576
Sample size
n = (z·σ/E)², rounded up

Method

  1. Decide between z (population σ known) and t (only the sample s, with n − 1 degrees of freedom).
  2. Find the critical value for the confidence level.
  3. Margin of error: E = critical value × standard deviation/√n.
  4. Interval: x̄ ± E.

Hypothesis tests & confidence intervals

z-test for a mean
z = (x̄ − μ0)/(σ/√n)
t-test for a mean
t = (x̄ − μ0)/(s/√n), df = n − 1
Two-sided at 5%
reject H0 if |z| > 1.96
Confidence interval
x̄ ± z·σ/√n

Method

  1. State H₀ and H₁ and decide whether the test is one- or two-sided.
  2. Calculate the test statistic: (x̄ − μ₀)/(σ/√n), or with s and t.
  3. Compare with the critical value, or find the p-value.
  4. Reject H₀ if the statistic is beyond the critical value (p below α), and state the conclusion in words.

Linear regression & correlation

Sums of squares
Sxx = Σ(x − x̄)² = Σx² − (Σx)²/n, Sxy = Σxy − ΣxΣy/n
Slope
b = Sxy/Sxx
Intercept
a = ȳ − b·x̄
Correlation
r = Sxy/√(SxxSyy)
Residual
e = y − ŷ

Chi-square tests

Test statistic
χ² = Σ(O − E)²/E
Goodness of fit
E = n·p, df = k − 1 − (estimated parameters)
Independence
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
57.580.5P = ?

The exam scores are normally distributed with mean 65 points and standard deviation 10 points. What fraction lies between 57.5 and 80.5 points? Give P as a decimal.

μ = 65σ = 10x1 = 57.5x2 = 80.5
P =
2.Normal distributionHard
557085100115130145pointsbottom 40%

The IQ scores are normally distributed with mean 100 points and standard deviation 15 points. Drag the cut-off so the shaded bottom 40% is to the left of it. Find the cut-off x.

μ = 100 pointsσ = 15 points40%
x = points
3.Confidence intervalsMedium
x̄E₁ = 1.690%, n = 100x̄E₂ = ?99%, n = 25

A 90% confidence interval for a mean, based on 100 observations and a known σ, has a margin of error of 1.6. What is the margin of error E₂ of a 99% interval based on 25 observations?

90%n₁ = 100E₁ = 1.699%n₂ = 25
E₂ =
4.Confidence intervalsHard
σ = 38, n = ?x̄ − Ex̄ + Ex̄E = 0.599%

The population standard deviation is 38. How large a sample n do you need so that a 99% confidence interval for the mean has a margin of error of at most 0.5?

σ = 38E = 0.599%
n =
5.Hypothesis tests & confidence intervalsMedium
t −2.01102.011

A sample of 49 students has a mean score of 494.9 and a sample standard deviation of 20. Find the test statistic t for H₀: μ = 500.

μ₀ = 500x̄ = 494.9s = 20n = 49
t =
6.Hypothesis tests & confidence intervalsHard
μ₀x̄c

A one-sided z test of H₀: μ = 100 against H₁: μ < 100 is done at α = 0.10 with a sample of 64; the standard deviation is known to be 24. In reality the mean is 94.3. Find the power 1 − β of the test in percent.

μ₀ = 100μ = 94.3σ = 24n = 64α = 0.10
1 − β = %
7.Linear regression & correlationMedium
xy1470177520101231032612729142xy15202530100150

The table gives 6 paired observations of x = temperature in °C and y = ice creams sold. Find the coefficient of determination r² of the least-squares line.

n = 6
r² =
8.Linear regression & correlationHard
xy2114136218261034xy510102030x = 7

The table gives 5 paired observations of x = load in kN and y = extension in mm. Use the least-squares line ŷ = a + b·x. Predict ŷ for x = 7.

n = 5x = 7
ŷ =
9.Chi-square testsMedium
busbikecarTotalstudents383844120staff2182040Total405664160

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 = 1602 × 3
χ² =
10.Chi-square testsHard
dfα = 0.05α = 0.0113.8416.63525.9919.21037.81511.34549.48813.277511.07015.086612.59216.812714.06718.475815.50720.090916.91921.6661018.30723.209

A chi-square goodness-of-fit test compares observed counts in 10 categories with fully specified expected proportions (no parameters are estimated from the data). Using the table, find the critical value χ²crit at significance level α = 0.05.

α = 0.05
χ²crit =

3Solutions

1.Normal distribution

Answer: P = 0.7128

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 = (57.5 − 65)/10 = −0.75
z₂ = (80.5 − 65)/10 = 1.55

Area between

P = Φ(1.55) − Φ(−0.75) = 0.9394 − 0.2266 = 0.7128

Answer

P = 0.7128
2.Normal distribution

Answer: x = 96.3 points

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) = 0.4

z from the table

z ≈ −0.25

Back to the original units

x = 100 − 0.25·15 = 96.3 points

Answer

x = 96.3 points
3.Confidence intervals

Answer: E₂ = 5.011

Confidence interval for a mean: x̄ ± margin of error. With a known population σ the margin is z·σ/√n; with only the sample standard deviation s, use t with n − 1 degrees of freedom: t·s/√n. Critical values: z = 1.645 (90%), 1.96 (95%), 2.576 (99%). To get a margin E or smaller, you need n ≥ (z·σ/E)², rounded up.

Ratio of margins

E₂/E₁ = (2.576/1.645)·√(100/25) = 3.132

New margin

E₂ = 1.6·3.132 = 5.011

Answer

E₂ = 5.011
4.Confidence intervals

Answer: n = 38329

Confidence interval for a mean: x̄ ± margin of error. With a known population σ the margin is z·σ/√n; with only the sample standard deviation s, use t with n − 1 degrees of freedom: t·s/√n. Critical values: z = 1.645 (90%), 1.96 (95%), 2.576 (99%). To get a margin E or smaller, you need n ≥ (z·σ/E)², rounded up.

Solve for n

n = (2.576·38/0.5)² = 38328

Round up

A sample size is a whole number, and rounding down would make the margin too large.

n = 38329

Answer

n = 38329
5.Hypothesis tests & confidence intervals

Answer: t = −1.785

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 = 20/√49 = 20/7 = 2.857

Test statistic

t = (494.9 − 500)/2.857 = −1.785

Answer

t = −1.785
t0.025; df = 48 = 2.011

|t| < 2.011, so H₀ is not rejected at the 5% level (two-sided).

6.Hypothesis tests & confidence intervals

Answer: 1 − β = 73.17 %

Power of a test: power = 1 − β is the probability of rejecting H₀ when it is false. Find the cutoff x̄c from μ₀ and z_α, then compute the probability of landing beyond x̄c using the true mean: z = (x̄c − μ)/(σ/√n).

Critical sample mean

SE = 24/√64 = 3
x̄c = 100 − 1.282·3 = 96.15

z under the true mean

z = (96.15 − 94.3)/3 = 0.618

Power

power = P(Z < 0.618) = 73.17%
1 − β = 73.17%

Answer

1 − β = 73.17%
7.Linear regression & correlation

Answer: r² = 0.9646

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 = 129/6 = 21.5
ȳ = 618/6 = 103

Sums of squares and products

Sxx = Σ(x − x̄)² = 157.5
Sxy = Σ(x − x̄)(y − ȳ) = 777
Syy = Σ(y − ȳ)² = 3974

Coefficient of determination

r² = 777²/(157.5·3974) = 0.9646

Answer

r² = 0.9646

About 96.46% of the variation in y is explained by the line.

8.Linear regression & correlation

Answer: ŷ = 23.95

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 = 30/5 = 6
ȳ = 105/5 = 21

Sums of squares and products

Sxx = Σ(x − x̄)² = 40
Sxy = Σ(x − x̄)(y − ȳ) = 118

Regression line

b = 118/40 = 2.95
a = 21 − 2.95·6 = 3.3
ŷ = 3.3 + 2.95·x

Prediction

ŷ = 3.3 + 2.95·7 = 23.95

Answer

ŷ = 23.95
9.Chi-square tests

Answer: χ² = 11.39

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.

Expected counts

students: 120·40/160 = 30, 120·56/160 = 42, 120·64/160 = 48
staff: 40·40/160 = 10, 40·56/160 = 14, 40·64/160 = 16

Chi-square statistic

χ² = (38 − 30)²/30 + (38 − 42)²/42 + (44 − 48)²/48 + (2 − 10)²/10 + (18 − 14)²/14 + (20 − 16)²/16
χ² = 11.39

Answer

χ² = 11.39

df = 2. At α = 0.05 the critical value is 5.991, so H₀ (independence) is rejected.

10.Chi-square tests

Answer: χ²crit = 16.92

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.

Degrees of freedom

df = 10 − 1 = 9

Table

χ²crit(df = 9, α = 0.05) = 16.919

Answer

χ²crit = 16.919

Reject H₀ if the computed χ² is larger than this value.

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