Відео

well explained ....❤‍🔥❤‍🔥❤‍🔥Thank you very much

Sir Jo experiment decide hote hai wo repeat kyon hota hai

It's a perfect video ,thank you and i hope you do more video with more information

Thank you.

Welldone Mam.

Where did the p-value come from?

- Kona introduces data analysis using Minitab. - Demonstrates pasting body part data into Minitab and organizing columns. - Analyzes the guest vs. actual hand size using scatter plots and calculates size differences. - Calculates relative size differences as percentages based on actual hand size. - Compares relative size differences by gender and age using descriptive statistics and visualizations.

Here is a summary of the "Ttest Minitab Examples" transcript: 1. **Data Overview:** - Data collected includes gender (male/female), hours worked per week (numeric), pretest score, and test score (both out of 100). 2. **Hypothesis Testing:** - Claim 1: Men work fewer hours per week than women. - Claim 2: There is a difference between pretest and test scores. - Claim 3: Women's test scores are different from men's test scores. - Claim 4: The average hours worked per week by students is less than the national standard of 40 hours. 3. **Statistical Tests:** - Claim 1: Two-sample t-test used for comparing men and women's hours worked per week. - Claim 2: Paired-samples t-test used for comparing pretest and test scores. - Claim 3: Two-sample t-test used for comparing men and women's test scores. - Claim 4: One-sample t-test used for comparing average hours worked per week to the national standard. 4. **Results and Decisions:** - Claim 1: The data did not provide enough evidence to support the claim that men work fewer hours per week than women. - Claim 2: The data provided enough evidence to support the claim that there is a difference between pretest and test scores. - Claim 3: The data did not provide enough evidence to support the claim that women's test scores are different from men's test scores. - Claim 4: The data provided enough evidence to support the claim that the average hours worked per week by students is less than the national standard of 40 hours. 5. **Summary of Approach:** - Utilized Minitab for statistical analysis, involving appropriate t-tests based on the nature of the claims and the data available.

- The transcript discusses using a bar chart to compare two categorical variables: hours of sleep and pet ownership. - The categorical variables are "sleep" (with categories less than 6 hours, 6-8 hours, and more than 8 hours) and "pet" (yes or no). - The bar chart is initially presented with "sleep" as the first grouping followed by "pet," illustrating the distribution of sleep hours for pet owners and non-pet owners. - The order of the categorical variables affects the chart's appearance and interpretation, highlighting differences in sleep patterns based on pet ownership. - The analysis suggests that individuals without pets are more likely to sleep over eight hours, while those with pets tend to sleep either 6-8 hours or less than six hours.

- Comparison using scatterplot between numeric (time spent exercising) and categorical (pet ownership) variables. - Purpose: Investigating the relationship between time spent exercising and daily water consumption for pet owners vs. non-pet owners. - Graph shows pet owners tend to drink more water compared to non-pet owners, suggesting a potential link between pet ownership and water consumption. - Observation of exercise levels indicates that non-pet owners tend to have lower exercise levels, hinting at a correlation between pet ownership and exercise habits. - Suggested follow-up: Perform descriptive statistics to compare mean glasses of water for pet owners versus non-pet owners, revealing pet owners consume more water on average.

- Video by Kona discussing scatterplots and analyzing two sets of data: exercise hours per week and glasses of water consumed daily. - Objective: Explore if there's a relationship between exercise time and water intake. - Demonstrated creating a scatterplot to visualize the data and interpreting the pattern. - Mentioned the regression line to determine the strength and direction of the relationship: positive relationship observed. - Encouraged using this information to facilitate discussions about data patterns and relationships.

- The transcript discusses comparing categorical data (gender and social media usage) using a table of counts and proportions. - The example question is whether social media use differs by gender. - Categorical variables for analysis are gender (female, male, other) and social media usage categories (0 hours, 1-3 hours, 4-6 hours). - The method involves using statistical analysis tools like cross-tabulation and chi-square. - The analysis calculates row and column percentages to understand proportions of social media usage among different genders.

- Video discusses analyzing numeric and categorical data using descriptive comparison and a boxplot. - Example question: Do the hours a person spends exercising differ based on pet ownership? - Variables used: Numeric variable (hours of exercise) and categorical variable (pet ownership: yes or no). - Descriptive statistics: Mean exercise hours differ between pet owners (7 hours) and non-pet owners (5.56 hours). - Boxplot analysis shows wider variability in exercise hours among pet owners compared to non-pet owners.

- Kona demonstrates basic descriptive statistics and categorical data analysis using Minitab. - Describes the distinction between numeric and categorical data and the need for appropriate analysis methods. - Guides opening an example dataset and understanding the data dictionary. - Explains how to perform descriptive statistics (mean, standard deviation, quartiles) for numeric data in Minitab. - Demonstrates tallying and displaying counts and percentages for categorical data using Minitab.

Here's a summary of the "Introduction to Minitab" transcript: 1. **Basic Layout of Minitab:** - Overview of the main components and tools, including Quick Start buttons and session window. - Ability to adjust screen size, rearrange, minimize, and maximize windows. 2. **Entering Data in Minitab:** - Explanation of how data is structured in Minitab with columns representing pieces of information and rows representing individuals. 3. **Categorical vs. Numeric Data:** - Differentiation between categorical data (text, denoted by "T") and numeric data (numbers, denoted by "C"). 4. **Recoding Data:** - How to recode data, converting between numeric and categorical variables using Minitab. 5. **Main Functions in Minitab:** - Overview of primary functions like file management, editing, calculations, statistics, and graphing within Minitab.

- The video discusses comparing average sleep hours based on pet ownership using a bar chart. - The focus is on exploring the difference in sleep duration for people with and without pets. - The variables involved are average hours of sleep (numeric) and pet ownership (categorical). - The presenter demonstrates creating a bar chart to visualize the mean hours of sleep for both groups. - It is emphasized that the choice of visualization (bar chart) might not always effectively convey the intended story; alternative visualizations may be considered for better representation of data.

Does anybody know how to make one solid block in a section ?

Thanks, that doesn't work when the two sets of categorical data have different lengths?

where can i install minitab for free?

🙏🙏🙏

Me from 4 years

really helpful refresh for an incoming exam thank you so much!!!!!! from Philippines

loved the way of your explaining keep going :)

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Excellent content

I need explain about flow chat of residual mean effective in minitab 18

Great job. Thank you very much.

000.1

I have a question regarding the Normal Q-Q plot. On the y-axes, does it show the quantiles of the residual distribution, or the residuals itself? On the x-axes it shows the quantiles of the residual distribution if it were normal, correct? Thank you for the video!

😍

Thanks lot

thanks

EXCELENT

Thanks❤️

Good presentation and easy understand. TQ

Terimakasih

Never used Minitab and this video helped me on my MBA project! Easy to follow. Thank you!

loved this video

Great Video thank you very much. Exactly what i was looking for !!!! So excided

good job, can share the file ?thank you

Nice video.

Hey I will pay you for your youtube channel. This is a serious offer I am not joking or trolling you. Please reply to this comment if this is possible. Thank you

I have a project due and I have to use Minitab and idk if I’m suppose to just use one quantitative and one qualitative

I’m confused

First video I've watched of minitab, I would have just kept it in excel...

Hey, thanks for your video. Is there anyway that I can download the data you use in the video to practice? Thank you!

tks for sharing this video.

can, you, please attach an excel data ?

It’s good if you start a statistics class from basic

Thank you!, you are really a life saver