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# How to Find Confidence Intervals in R (With Examples)

A confidence interval is a range of values that is likely to contain a population parameter with a certain level of confidence.

It is calculated using the following general formula:

Confidence IntervalÂ = (point estimate)Â  +/-Â  (critical value)*(standard error)

This formula creates an interval with a lower bound and an upper bound, which likely contains a population parameter with a certain level of confidence:

Confidence IntervalÂ Â = [lower bound, upper bound]

This tutorial explains how to calculate the following confidence intervals in R:

1. Confidence Interval for a Mean

2. Confidence Interval for a Difference in Means

3. Confidence Interval for a Proportion

4. Confidence Interval for a Difference in Proportions

Letâ€™s jump in!

### Example 1: Confidence Interval for a Mean

We use the following formula to calculate a confidence interval for a mean:

Confidence Interval = xÂ  +/-Â  tn-1, 1-Î±/2*(s/âˆšn)

where:

• x:Â sample mean
• t: the t-critical value
• s:Â sample standard deviation
• n:Â sample size

Example:Â Suppose we collect a random sample of turtles with the following information:

• Sample sizeÂ n = 25
• Sample mean weightÂ xÂ = 300
• Sample standard deviationÂ s = 18.5

The following code shows how to calculate a 95% confidence interval for the true population mean weight of turtles:

```#input sample size, sample mean, and sample standard deviation
n #calculate margin of error
margin #calculate lower and upper bounds of confidence interval
low ```

The 95% confidence interval for the true population mean weight of turtles is [292.36, 307.64].

### Example 2: Confidence Interval for a Difference in Means

We use the following formula to calculate a confidence interval for a difference in population means:

Confidence intervalÂ = (x1â€“x2) +/- t*âˆš((sp2/n1) + (sp2/n2))

where:

• x1,Â x2: sample 1 mean, sample 2 mean
• t: the t-critical value based on the confidence level and (n1+n2-2) degrees of freedom
• sp2: pooled variance, calculated as ((n1-1)s12Â +Â (n2-1)s22) / (n1+n2-2)
• t: the t-critical value
• n1, n2: sample 1 size, sample 2 size

Example: Suppose we want to estimate the difference in mean weight between two different species of turtles, so we go out and gather a random sample of 15 turtles from each population. Here is the summary data for each sample:

Sample 1:

• x1Â = 310
• s1Â = 18.5
• n1Â = 15

Sample 2:

• x2Â = 300
• s2Â = 16.4
• n2Â = 15

The following code shows how to calculate a 95% confidence interval for the true difference in population means:

```#input sample size, sample mean, and sample standard deviation
n1 #calculate pooled variance
sp = ((n1-1)*s1^2 + (n2-1)*s2^2) / (n1+n2-2)

#calculate margin of error
margin #calculate lower and upper bounds of confidence interval
low ```

The 95% confidence interval for the true difference in population means isÂ [-3.06, 23.06].

### Example 3: Confidence Interval for a Proportion

We use the following formula to calculate a confidence interval for a proportion:

Confidence Interval = pÂ  +/-Â  z*(âˆšp(1-p) / n)

where:

• p:Â sample proportion
• z:Â the chosen z-value
• n:Â sample size

Example: Suppose we want to estimate the proportion of residents in a county that are in favor of a certain law. We select a random sample of 100 residents and ask them about their stance on the law. Here are the results:

• Sample sizeÂ n = 100
• Proportion in favor of lawÂ p = 0.56

The following code shows how to calculate a 95% confidence interval for the true proportion of residents in the entire county who are in favor of the law:

```#input sample size and sample proportion
n

#calculate margin of error
margin #calculate lower and upper bounds of confidence interval
low ```

The 95% confidence interval for the true proportion of residents in the entire county who are in favor of the law isÂ [.463, .657].

### Example 4: Confidence Interval for a Difference in Proportions

We use the following formula to calculate a confidence interval for a difference in proportions:

Confidence interval = (p1â€“p2)Â  +/-Â  z*âˆš(p1(1-p1)/n1Â + p2(1-p2)/n2)

where:

• p1, p2: sample 1 proportion, sample 2 proportion
• z: the z-critical value based on the confidence level
• n1, n2: sample 1 size, sample 2 size

Example: Suppose we want to estimate the difference in the proportion of residents who support a certain law in county A compared to the proportion who support the law in county B. Here is the summary data for each sample:

Sample 1:

• n1Â = 100
• p1Â = 0.62 (i.e. 62 out of 100 residents support the law)

Sample 2:

• n2Â = 100
• p2Â = 0.46 (i.e. 46 our of 100 residents support the law)

The following code shows how to calculate a 95% confidence interval for the true difference in proportion of residents who support the law between the counties:

```#input sample sizes and sample proportions
n1

#calculate margin of error
margin #calculate lower and upper bounds of confidence interval
low ```

The 95% confidence interval for the true difference in proportion of residents who support the law between the counties isÂ [.024, .296].

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