Descriptive Statistics In Excel: Detailed Explanation With Example

Descriptive Statistics Excel - detailed explanation with examples, Descriptive Statistics In Excel with Graphical Results

Hello Friends,
Descriptive Statistics is a primary tool used to provide information about the central tendency and variability of your data. I have explained it in detail with the help of practical examples and graphical analysis.

This video consists of the following topics:
✅ Introduction to Descriptive Statistics (Data Analysis In Excel)
✅ Population and Sample
✅ Population parameters and Sample parameters
✅ Measures of Central Tendency
✅ Measures of Variation (Measures of Dispersion)
✅ What is Descriptive Statistics?
✅ Example to perform Descriptive Statistics
✅ Detailed procedure to perform Descriptive Statistics in Excel
✅ Interpretation of results from Descriptive Statistics
✅ Descriptive Statistics by variable in Microsoft Excel
✅ Interpretation of results from Descriptive Statistics by variable
✅ Descriptive Statistics with Graphical Analysis
✅ Learn Lean Six Sigma and Minitab Most Effectively and Practically
I am sure you will like it.

The Important links about LEARN & APPLY:

0:00 Introduction to Descriptive Statistics (Data Analysis In Excel)
0:36 Population and Sample
1:39 Population parameters and Sample parameters
2:20 Measures of Central Tendency
3:00 Measures of Variation (Measures of Dispersion)
3:56 What is Descriptive Statistics?
4:21 Example to perform Descriptive Statistics
5:19 Detailed procedure to perform Descriptive Statistics in Excel
6:26 Interpretation of results from Descriptive Statistics
7:21 Descriptive Statistics by variable in Microsoft Excel
8:21 Interpretation of results from Descriptive Statistics by variable
9:12 Descriptive Statistics with Graphical Analysis
11:16 Learn Lean Six Sigma and Minitab Most Effectively and Practically

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Descriptive statistics with Example:
Population and sample:
1. Population consists of all data points. The sample represents a certain number of parts of the population.
2. Sample statistics are not population parameters but estimates of population parameters.
3. Most often, it is not possible to collect data on the whole population due to high cost and inpracticality.
4. We calculate sample statistics to conclude population parameters.
5. Population is characterised by parameters such as mean u, standard deviation, variance S2
6. We calculate statistics from the sample such as mean, standard deviation S2, variance S2 etc.
Measures of Central Tendency:
There are 3 measures of central tendency,
1. Mean
2. Mode
3. Median
Measures of variation ( Measures of Dispersion)
The extent of the spread of the values from the mean value is called dispersion .
The Measures of dispersion are
1. Range (R)
2. Standard Deviation (S)
3. Variance ( S2)
4. Coefficient of Variation (Cov)
Descriptive statistics:
The Descriptive statistics analysis tool generates a report of universal a report of univariate stastics for data in the input range, providing about the central tendency of your data.
Descriptive Stastics Example:
A Quality Control Engineer needs to ensure that the caps of shampoo bottles are fastened correctly.
If the caps are fastened too loosely, they may fall off during shipping. If they are fastened too tightly, they may be too difficult to remove.
The target torque value for fastening the caps is 18.
The engineer collects a random samples of 68 bottles and tests are the amount of torque that is needed to remove the caps.