# Using the Confidence

Interval in Excel To

Find the True Range

# of Average Sales

This article will show you exactly how to calculate a 95% Confidence Interval in Excel of daily sales for a commercial web site. The 95% Confidence Interval is the interval for which we are 95% sure that the true mean of daily sales lies within.

The larger the sample size, the more accurate and smaller the confidence interval will be. We are not taking a random sample in this case, but are using the most recent 60 days of sales data.

The advantages of statistical analysis in Excel to solve business statistics problems is that most problems can be solved in just one or two steps and there is no more need to look anything up on Normal Distribution tables.

The 95% Confidence Interval calculated here is a Confidence Interval for Means, not Proportion. A Confidence Interval for Proportion uses an entirely different set of statistical formulas. Another article in this blog shows exactly how to calculate a Confidence Interval of Proportion. Here is the problem:

**Problem: Average daily demand for a product sold on a web site is 455 units sold with a standard deviation of 200. This average and standard deviation are taken from sale data collected every day for the most recent 60 days. What is the range that the true average daily product sales lies in with 95% certainty?**

Before we begin solving this problem, we need to know about creating Confidence Intervals in Excel. Here is a brief lesson:

**Basic Explanation of Confidence Intervals**

The Confidence Interval is an interval in which the true population mean or proportion probably lies based upon a much smaller random sample taken from that population.

A 95% Confidence Interval of a Mean is the interval that has a 95% chance of containing the true population mean.

The width of a Confidence Interval is affected by the sample size. The larger the sample size, the more accurate and tighter is the estimate of the true population mean. The larger the sample size, the smaller will be the Confidence Interval. Samples taken must be random and also be representative of the population.

**Calculate Confidence Intervals Using Large Samples (n is greater than 30)**

Confidence Intervals are usually calculated and plotted on a Normal curve. If the sample size is less then 30, the population must be known to be Normally distributed. If small-sample data (n less than 30) is used to plot the Confidence Interval of the Mean for a population that is not Normally distributed, the result can be totally wrong.

Probably the most common major mistake in statistics is to apply Normal or t-distribution tests to small-sample data taken from a population of unknown distribution.

Typically the actual distribution of a population is not known. If the population's underlying distribution is not known (usually it is not), then only large samples (n greater than 30) are valid for creating a Confidence Interval of the Mean. The most important theorem of statistics, the Central Limit Theorem, explains the reason for this.

**The Central Limit Theorem**

The Central Limit Theorem is statistics' most fundamental theorem. In a nutshell, it states the following: Random sample data can be plotted on a Normal curve to estimate a population's mean no matter how the population is distributed, as long as sample size is large (n greater than 30).

The above definition of the Central Limit Theorem is the most practical and easy-to-understand. The following definition of this theorem is a bit more technical and will satisfy statisticians (but basically says the same thing as the above): No matter how the population is distributed, the sampling distribution of the mean approaches the Normal curve as sample size becomes large.

**Confidence Interval of a Population Mean**

Creating a Confidence Interval for a population's mean is very similar to creating a Confidence Interval for a population's proportion. The only real difference is how the standard error is calculated. Everything else is the same.

**Mean Sampling vs. Proportion Sampling**

What determines whether a mean is being estimated or a proportion is being estimated is the number of possible outcomes of each sample taken.

**. For example, if you are comparing the proportion of click-through visitors in two different PPC ads who converted (purchased), each sample has only two possible values; the visitor sampled either converted (purchased) or they didn't.**

Proportion samples have only two possible outcomes

Proportion samples have only two possible outcomes

**. For example, if you are comparing the mean sales of salespeople in two different cities, each salesperson's sales sampled can have numerous values.**

Mean samples have multiple possible outcomes

Mean samples have multiple possible outcomes

Below is a description of how to calculate a Confidence Interval for a population's mean. Note that everything is almost the same as the calculation of the Confidence Interval for a proportion, except sample standard error.

**Levels of Confidence and Significance**

**, α ("alpha"), equals the maximum allowed percent of error. If the maximum allowed error is 5%, then α = 0.05.**

Level of Significance

Level of Significance

**is selected by the user. A 95% Level is the most common. A 95% Confidence Level would correspond to a 95% Confidence Interval of the Proportion.**

Level of Confidence

Level of Confidence

This would state that the actual population Proportion has a 95% probability of lying within the calculated interval. A 95% Confidence Level corresponds to a 5% Level of Significance, or α = 0.05. The Confidence Level therefore equals 1 - α.

**Population Proportion vs. Sample Proportion**

**= µ (Greek letter "mu") - This is what we are trying to estimate.**

Population Mean

Population Mean

**= x**

Sample Mean

Sample Mean

**avg**

**= n**

Sample Size

Sample Size

Standard Deviation and Standard Error

Standard Deviation and Standard Error

**Standard Deviation**is a measure of statistical dispersion. It's formula is the following:

SQRT ( [ SUM { (x - x

**avg**)^2 } ] / n).

There is no need to memorize the formula because you can plug in Excel's

**STDEV**function discussed later. Standard Deviation equals the square root of the Variance.

**Population Standard Deviation**= σ ("sigma")

**Sample Standard Deviation**= s

**is an estimate of population Standard Deviation from data taken from a sample. If the population Standard Deviation, σ, is known, then the Sample Standard Error, sxavg, can be calculated.**

Standard Error

Standard Error

If only the Sample Standard Deviation, s, is known, then Sample Standard Error, s

**xavg**, can be estimated by substituting Sample Standard Deviation, s, for Population Standad Deviation, σ, as follows:

**Sample Standard Error**= s

**xavg**= σ / SQRT(n) ≈ s / SQRT(n)

Region of Certainty vs. Region of Uncertainty

Region of Certainty vs. Region of Uncertainty

**is the area under the Normal curve that corresponds to the required Level of Confidence. If a 95% percent Level of Confidence is required, then the Region of Certainty will contain 95% of the area under the Normal curve. The outer boundaries of the Region of Certainty will be the outer boundaries of the Confidence Interval.**

Region of Certainty

Region of Certainty

The Region of Certainty, and therefore the Confidence Interval, will be centered about the mean. Half of the Confidence Interval is on one side of the mean and half on the other side.

**is the area under the Normal curve that is outside of the Region of Certainty. Half of the Region of Uncertainty will exist in the right outer tail of the Normal curve and the other half in the left outer tail. This is similar to the concept of the "two-tailed test" that is used in Hypothesis testing in further sections of this course. The concepts of one and two-tailed testing are not used when calculating Confidence Intervals. Just remember that the Region of Certainty, and therefore the Confidence Interval, are always centered about the mean on the Normal curve.**

Region of Uncertainty

Region of Uncertainty

**Relationship Between Region of Certainty, Uncertainty, and Alpha**

The Region of Uncertainty corresponds to α ("alpha"). If α = 0.05, then that Region of Uncertainty contains 5% of the area under the Normal curve. Half of that area (2.5%) is in each outer tail. The 95% area centered about the mean will be the Region of Certainty. The outer boundaries of this Region of Certainty will be the outer boundaries of the 95% Confidence Interval.

The Level of Confidence is 95% and the Level of Significance, or maximum error allowed, is 5%.

**Z Score**

Z Score is the number of Standard Errors from the mean to outer right boundary of the Region of Certainty (and therefore to the outer right boundary of the Confidence Interval).

Standard Errors are used and not Standard Deviations because sample data is being used to calculate the Confidence Interval.

Z Score is calculated by the following Excel function:

Z Score(1-α) = NORMSINV (1 - α/2) - This will be discussed shortly.

**Excel Functions Used When Calculating Confidence Interval for a Population Mean**

Excel functions that are used are listed and are highlighted in

**BOLD**::

**(Highlighted block of cells) = Sample size = n**

COUNT

COUNT

This counts the number of cells in highlighted block

**(1 - α/2) = Z Score(1 - α)**

NORMSINV

NORMSINV

= Number of Standard Errors from mean to boundary of Confidence Interval.

Note that (1 - α/2) = the entire area in the Normal curve to the left of outer right boundary of the Region of Certainty, or Confidence Interval. This includes the entire Region of Certainty and the half of the Region of Uncertainty that exists in the left tail.

For example:

**Level of Confidence**= 95% for a 95% Confidence Interval

**Level of Significance**= 5% (α = 0.05)

1 - α = 0.95 = 95%

Z Score

**95%**= NORMSINV (1 - α/2) = NORMSINV (1 - .05/2) = NORMSINV(1 - 0.025)

Z Score

**95%**= NORMSINV (0.975) = 1.96

The outer right boundary of the 95% Confidence Interval, and the Region of Certainty, is 1.96 Standard Errors from the mean. The left boundary is the same distance from the mean because the Confidence Interval is centered about the mean.

**Formulas for Calculating Confidence Interval Boundaries from Sample Data for a Population Mean**

**= Sample proportion +/- Z Score(1-α) * Sample Standard Error**

Confidence Interval Boundaries

Confidence Interval Boundaries

**Confidence Interval Boundaries**= x

**avg**+/- Z Score(1-α) * sx

**avg**

**Sample Mean**= x

**avg**

Z Score(1 - α) = NORMSINV (1 - α/2)

**Sample size**= n = COUNT (Highlighted block of cells containing samples)

**Confidence Interval Boundaries**= x

**avg**+/- Z Score(1-α) * sx

**avg**

**(Highlighted block of cells) = Sample size = n**

COUNT

COUNT

- Counts number of cells in highlighted block

**STDEV**(Highlighted block of cells) = Standard deviation

- Calculates Standard Deviation of all cells in highlighted block

**AVERAGE**(Highlighted block of cells) = Mean

- Calculates the mean of all cells in highlighted block

**NORMSINV**(1 - α/2) = Z Score(1 - α)

= Number of Standard errors from mean to boundary of Confidence Interval. Note that (1 - α/2)

= the entire area in the Normal curve to the left of outer right boundary of the Region of Certainty, or Confidence Interval. This includes the entire Region of Certainty and the half of the Region of Uncertainty that exists in the left tail.

For example:

**Level of Confidence**= 95% for a 95% Confidence Interval

**Level of Significance**= 5% (α = 0.05)

1 - α = 0.95 = 95%

Z Score

**95%**= NORMSINV(1 – α/2) = NORMSINV (1 - .05/2) = NORMSINV(1 - 0.025)

Z Score

**95%**= NORMSINV (0.975) = 1.96

The outer right boundary of the 95% Confidence Interval, and the Region of Certainty, is 1.96 Standard Errors from the mean. The left boundary is the same distance from the mean because the Confidence Interval is centered about the mean.

**CONFIDENCE**( α, s, n ) =

**Width of half of the Confidence Interval**

α = Level of Significance

s = Sample Standard Deviation - Note that this is not Standard Error.

s is calculated by applying

**STDEV**to the sample values.

n = Sample size - Apply

**COUNT**to sample values.

**Confidence Interval Boundaries**= Sample mean +/- Z Score(1-α) * Sample Standard Error

**Confidence Interval Boundaries**= x

**avg**+/- Z Score(1-α) * sx

**avg**

**= x**

Sample Mean

Sample Mean

**avg**=

**AVERAGE**(Highlighted block of cell containing samples)

Z Score(1 - α) =

**NORMSINV**(1 - α/2)

**Sample Standard Error**= sx

**avg**= σ / SQRT(n) ≈ s / SQRT(n)

**Sample Size**= n =

**COUNT**(Highlighted block of cells containing samples)

**Sample Standard Deviation**= s =

**STDEV**(Highlighted block of cells containing samples)

**CONFIDENCE**( α, s, n ) =

**Width of half of the Confidence Interval**

**( α, s, n ) = Z Score(1-α) * sx**

CONFIDENCE

CONFIDENCE

**avg**

So:

**Confidence Interval Boundaries**

= x

**avg**+/- Z Score(1-α) * sx

**avg**

Confidence Interval Boundaries

Confidence Interval Boundaries

= x

**avg**+/-

**CONFIDENCE**( α, s, n )

*Click On Image To See Larger Version*

Problem: Calculate a Confidence Interval of Daily Internet Sales from a Single Web Site Based Upon Mean and Standard Deviation from a Sample of the most recent Daily Sales.

Problem: Calculate a Confidence Interval of Daily Internet Sales from a Single Web Site Based Upon Mean and Standard Deviation from a Sample of the most recent Daily Sales.

**Average daily demand for a product sold on a web site is 455 units sold with a standard deviation of 200. This average and standard deviation are taken from sale data collected every day for the most recent 60 days. What is the range that the true average daily product sales lies in with 95% certainty?**

**Level of Significance**= α = 0.05

**Sample Size**= n = 60

**Sample Mean**= x

**avg**= 455

**Sample Standard Deviation**= s = 200

**Sample Standard Error**= sx

**avg**= σ / SQRT(n) ≈ s / SQRT(n)

**avg**≈ s /

**SQRT**(n) = 200 /

**SQRT**(60) = 25.8

**95%**=

**NORMSINV**(1 - α/2)

**NORMSINV**(1 - 0.025) =

**NORMSINV**(0.975) = 1.96

**Width of Half the Confidence Interval**=

**CONFIDENCE**( α, s, n)

**CONFIDENCE**( 0.05, 200, 60) = 50.6

**Width of Half the Confidence Interval**= Z Score(1-α) * sx

**avg**

**Confidence Interval Boundaries**= x

**avg**+/- Z Score(1-α) * sx

**avg**

**Confidence Interval Boundaries**= x

**avg**+/-

**CONFIDENCE**( α, s, n)

*Click Image To See Larger Version*Create a Confidence Interval in Excel To Find Your True Daily Sales

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**Excel Master Series Blog Directory**

Statistical Topics and Articles In Each Topic

- Histograms in Excel
- Bar Chart in Excel
- Combinations & Permutations in Excel
- Normal Distribution in Excel
- Overview of the Normal Distribution
- Normal Distribution’s PDF (Probability Density Function) in Excel 2010 and Excel 2013
- Normal Distribution’s CDF (Cumulative Distribution Function) in Excel 2010 and Excel 2013
- Solving Normal Distribution Problems in Excel 2010 and Excel 2013
- Overview of the Standard Normal Distribution in Excel 2010 and Excel 2013
- An Important Difference Between the t and Normal Distribution Graphs
- The Empirical Rule and Chebyshev’s Theorem in Excel – Calculating How Much Data Is a Certain Distance From the Mean
- Demonstrating the Central Limit Theorem In Excel 2010 and Excel 2013 In An Easy-To-Understand Way

- t-Distribution in Excel
- Binomial Distribution in Excel
- z-Tests in Excel
- t-Tests in Excel
- Overview of t-Tests: Hypothesis Tests that Use the t-Distribution
- 1-Sample t-Tests in Excel
- Overview of the 1-Sample t-Test in Excel 2010 and Excel 2013
- Excel Normality Testing For the 1-Sample t-Test in Excel 2010 and Excel 2013
- 1-Sample t-Test – Effect Size in Excel 2010 and Excel 2013
- 1-Sample t-Test Power With G*Power Utility
- Wilcoxon Signed-Rank Test As a 1-Sample t-Test Alternative in Excel 2010 and Excel 2013
- Sign Test As a 1-Sample t-Test Alternative in Excel 2010 and Excel 2013

- 2-Independent-Sample Pooled t-Tests in Excel
- Overview of 2-Independent-Sample Pooled t-Test in Excel 2010 and Excel 2013
- Excel Variance Tests: Levene’s, Brown-Forsythe, and F Test For 2-Sample Pooled t-Test in Excel 2010 and Excel 2013
- Excel Normality Tests Kolmogorov-Smirnov, Anderson-Darling, and Shapiro Wilk Tests For Two-Sample Pooled t-Test
- Two-Independent-Sample Pooled t-Test - All Excel Calculations
- 2-Sample Pooled t-Test – Effect Size in Excel 2010 and Excel 2013
- 2-Sample Pooled t-Test Power With G*Power Utility
- Mann-Whitney U Test in Excel as 2-Sample Pooled t-Test Nonparametric Alternative in Excel 2010 and Excel 2013
- 2-Sample Pooled t-Test = Single-Factor ANOVA With 2 Sample Groups

- 2-Independent-Sample Unpooled t-Tests in Excel
- 2-Independent-Sample Unpooled t-Test in Excel 2010 and Excel 2013
- Variance Tests: Levene’s Test, Brown-Forsythe Test, and F-Test in Excel For 2-Sample Unpooled t-Test
- Excel Normality Tests Kolmogorov-Smirnov, Anderson-Darling, and Shapiro-Wilk For 2-Sample Unpooled t-Test
- 2-Sample Unpooled t-Test Excel Calculations, Formulas, and Tools
- Effect Size for a 2-Independent-Sample Unpooled t-Test in Excel 2010 and Excel 2013
- Test Power of a 2-Independent Sample Unpooled t-Test With G-Power Utility

- Paired (2-Sample Dependent) t-Tests in Excel
- Paired t-Test in Excel 2010 and Excel 2013
- Excel Normality Testing of Paired t-Test Data
- Paired t-Test Excel Calculations, Formulas, and Tools
- Paired t-Test – Effect Size in Excel 2010, and Excel 2013
- Paired t-Test – Test Power With G-Power Utility
- Wilcoxon Signed-Rank Test As a Paired t-Test Alternative
- Sign Test in Excel As A Paired t-Test Alternative

- Hypothesis Tests of Proportion in Excel
- Hypothesis Tests of Proportion Overview (Hypothesis Testing On Binomial Data)
- 1-Sample Hypothesis Test of Proportion in Excel 2010 and Excel 2013
- 2-Sample Pooled Hypothesis Test of Proportion in Excel 2010 and Excel 2013
- How To Build a Much More Useful Split-Tester in Excel Than Google's Website Optimizer

- Chi-Square Independence Tests in Excel
- Chi-Square Goodness-Of-Fit Tests in Excel
- F Tests in Excel
- Correlation in Excel
- Pearson Correlation in Excel
- Spearman Correlation in Excel
- Confidence Intervals in Excel
- Overview of z-Based Confidence Intervals of a Population Mean in Excel 2010 and Excel 2013
- t-Based Confidence Intervals of a Population Mean in Excel 2010 and Excel 2013
- Minimum Sample Size to Limit the Size of a Confidence interval of a Population Mean
- Confidence Interval of Population Proportion in Excel 2010 and Excel 2013
- Min Sample Size of Confidence Interval of Proportion in Excel 2010 and Excel 2013

- Simple Linear Regression in Excel
- Overview of Simple Linear Regression in Excel 2010 and Excel 2013
- Simple Linear Regression Example in Excel 2010 and Excel 2013
- Residual Evaluation For Simple Regression in Excel 2010 and Excel 2013
- Residual Normality Tests in Excel – Kolmogorov-Smirnov Test, Anderson-Darling Test, and Shapiro-Wilk Test For Simple Linear Regression
- Evaluation of Simple Regression Output For Excel 2010 and Excel 2013
- All Calculations Performed By the Simple Regression Data Analysis Tool in Excel 2010 and Excel 2013
- Prediction Interval of Simple Regression in Excel 2010 and Excel 2013

- Multiple Linear Regression in Excel
- Basics of Multiple Regression in Excel 2010 and Excel 2013
- Multiple Linear Regression Example in Excel 2010 and Excel 2013
- Multiple Linear Regression’s Required Residual Assumptions
- Normality Testing of Residuals in Excel 2010 and Excel 2013
- Evaluating the Excel Output of Multiple Regression
- Estimating the Prediction Interval of Multiple Regression in Excel
- Regression - How To Do Conjoint Analysis Using Dummy Variable Regression in Excel

- Logistic Regression in Excel
- Logistic Regression Overview
- Logistic Regression Performed in Excel 2010 and Excel 2013
- R Square For Logistic Regression Overview
- Excel R Square Tests: Nagelkerke, Cox and Snell, and Log-Linear Ratio in Excel 2010 and Excel 2013
- Likelihood Ratio Is Better Than Wald Statistic To Determine if the Variable Coefficients Are Significant For Excel 2010 and Excel 2013
- Excel Classification Table: Logistic Regression’s Percentage Correct of Predicted Results in Excel 2010 and Excel 2013
- Hosmer-Lemeshow Test in Excel – Logistic Regression Goodness-of-Fit Test in Excel 2010 and Excel 2013

- Single-Factor ANOVA in Excel
- Overview of Single-Factor ANOVA
- Single-Factor ANOVA Example in Excel 2010 and Excel 2013
- Shapiro-Wilk Normality Test in Excel For Each Single-Factor ANOVA Sample Group
- Kruskal-Wallis Test Alternative For Single Factor ANOVA in Excel 2010 and Excel 2013
- Levene’s and Brown-Forsythe Tests in Excel For Single-Factor ANOVA Sample Group Variance Comparison
- Single-Factor ANOVA - All Excel Calculations
- Overview of Post-Hoc Testing For Single-Factor ANOVA
- Tukey-Kramer Post-Hoc Test in Excel For Single-Factor ANOVA
- Games-Howell Post-Hoc Test in Excel For Single-Factor ANOVA
- Overview of Effect Size For Single-Factor ANOVA
- ANOVA Effect Size Calculation Eta Squared (?2) in Excel 2010 and Excel 2013
- ANOVA Effect Size Calculation Psi (?) – RMSSE – in Excel 2010 and Excel 2013
- ANOVA Effect Size Calculation Omega Squared (?2) in Excel 2010 and Excel 2013
- Power of Single-Factor ANOVA Test Using Free Utility G*Power
- Welch’s ANOVA Test in Excel Substitute For Single-Factor ANOVA When Sample Variances Are Not Similar
- Brown-Forsythe F-Test in Excel Substitute For Single-Factor ANOVA When Sample Variances Are Not Similar

- Two-Factor ANOVA With Replication in Excel
- Two-Factor ANOVA With Replication in Excel 2010 and Excel 2013
- Variance Tests: Levene’s and Brown-Forsythe For 2-Factor ANOVA in Excel 2010 and Excel 2013
- Shapiro-Wilk Normality Test in Excel For 2-Factor ANOVA With Replication
- 2-Factor ANOVA With Replication Effect Size in Excel 2010 and Excel 2013
- Excel Post Hoc Tukey’s HSD Test For 2-Factor ANOVA With Replication
- 2-Factor ANOVA With Replication – Test Power With G-Power Utility
- Scheirer-Ray-Hare Test Alternative For 2-Factor ANOVA With Replication

- Two-Factor ANOVA Without Replication in Excel
- Creating Interactive Graphs of Statistical Distributions in Excel
- Interactive Statistical Distribution Graph in Excel 2010 and Excel 2013
- Interactive Graph of the Normal Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the Chi-Square Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the t-Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the Binomial Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the Exponential Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the Beta Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the Gamma Distribution in Excel 2010 and Excel 2013
- Interactive Graph of the Poisson Distribution in Excel 2010 and Excel 2013

- Solving Problems With Other Distributions in Excel
- Solving Uniform Distribution Problems in Excel 2010 and Excel 2013
- Solving Multinomial Distribution Problems in Excel 2010 and Excel 2013
- Solving Exponential Distribution Problems in Excel 2010 and Excel 2013
- Solving Beta Distribution Problems in Excel 2010 and Excel 2013
- Solving Gamma Distribution Problems in Excel 2010 and Excel 2013
- Solving Poisson Distribution Problems in Excel 2010 and Excel 2013

- Optimization With Excel Solver
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- Optimal Investment Selection With Excel Solver
- Minimizing the Total Cost of Shipping From Multiple Points To Multiple Points With Excel Solver
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- Chi-Square Population Variance Test in Excel
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