At 05:53:53, Sumit mistakenly stated that the values fall within the acceptance region. Since 34325 is less than 34917, the value will fall within the rejection region, and thus, we will reject the null hypothesis. We hope that helps. Thank You!
00:05 Statistics is broadly classified into descriptive, inferential, and hypothesis 03:08 Descriptive statistics is used to summarize and describe data in a concise manner 09:28 Finding the average study hours in 12th class 12:15 The practical solution is to take a random sample of students for surveying. 18:18 The video discusses the process of statistically proving claims or statements. 21:46 Understanding the distinction between numerical and categorical variables. 27:42 Educational Qualification and Quantitative Variables 30:43 Income and age are continuous quantitative variables with infinite possible values. 36:18 Descriptive statistics include measures of central tendency and measures of variation. 39:24 Mean or average is a measure of central tendency. 44:37 Identifying and handling outliers in data 47:36 Understanding the concept of median 53:10 Mode is the observation with the highest occurrence in the data. 55:53 Understanding measures of central tendency 1:01:20 Subject A has low variation around the average line, while Subject B has high variation. 1:04:19 Measures of variation describe data scatteredness around the center or average line. 1:10:11 Calculating the average and marking the data points around the center 1:12:54 Understanding distance and average deviation from the center 1:18:32 Understanding variance and standard deviation in statistics 1:21:28 Standard deviation shows data spread from the mean. 1:27:05 Standard deviation alone cannot compare variation, use coefficient of variation. 1:29:54 Comparing variation using coefficient of variation 1:35:50 Product number two is very stable in the market 1:38:03 Descriptive statistics helps to explain and conclude data using various measures. 1:43:25 Understanding leverage data point and probability 1:45:55 Uncertainty in random experiments 1:51:25 Understanding equally likely outcomes in sample space. 1:53:51 Probability is influenced by equally likely sample spaces 1:59:20 Sum of all probabilities over the sample space is always equal to 1 2:02:01 Probability of events A and B and their union 2:07:11 Understanding the structure of playing cards and calculating probabilities. 2:09:58 Calculating probability of getting specific cards from a deck 2:15:28 Understanding probability rules and their applications. 2:18:16 Probability Rule: Probability of an event and its complement add up to 1 2:23:20 First toss outcome doesn't affect second toss probability. 2:25:54 Understanding the Gambler's Fallacy 2:31:20 Probability calculation for multiple draws 2:34:06 Probability of dependent events 2:39:57 Random variables quantify the outcome of an experiment. 2:43:03 Random variables can be discrete or continuous. 2:48:59 Continuous random variables can take endless values and are known as The Continuous random variable. 2:51:31 Calculating the Expected Value of a Random Variable 2:57:14 Understanding expected value in probability 2:59:42 Understanding distributions for discrete random variables 3:05:18 Probability of obtaining heads from coin tosses 3:08:03 Observing and categorizing data for random variables 3:13:18 Probability density function deals with ranges, not specific points 3:15:49 Standard discrete and continuous distributions are important in statistics. 3:21:38 Calculating the probability of a binomial experiment 3:24:26 Conducting the experiment to find the probability of getting exactly two orange balls in three trials. 3:29:38 Calculating probability for binomial experiments 3:32:33 Understanding probability and binomial experiments. 3:37:47 Calculating the probability using combination formula and plotting the probabilities on a column chart. 3:40:35 Probability of hitting at least 5 times is 36% 3:45:41 Standard Normal Distribution and Z-scale 3:48:13 Scaling is the process of converting data into a common scale for comparison. 3:53:07 Calculating minimum and maximum values for different columns 3:56:28 Understanding the normalized scale for values close to zero and one 4:02:24 Z scaling converts data to mean zero and standard deviation one unit. 4:04:41 Standard normal distribution properties explained. 4:09:21 The data distribution is close to standard normal distribution but not exactly the same. 4:11:58 Calculating percentages and proving properties of standard deviation 4:17:41 Understanding Z table for finding cumulative probability 4:20:33 Analyzing the probability of data points within specific ranges. 4:26:19 Using Z tables to find probability and Z scores 4:28:51 Analyzing normal distribution and probability calculation 4:34:20 Right skewness is caused by outliers, affecting mean position. 4:37:13 Normal distribution properties and their applicability 4:42:35 Survey is essential for data collection. 4:45:13 Using sample statistic to estimate population mean 4:50:55 Central Limit Theorem properties 4:53:38 Sampling distribution closely resembles a normal distribution 4:58:39 Estimating population parameter range from sample mean 00:01 Understanding the significance of range and confidence interval in statistics 5:06:50 Hypothesis testing involves two assumptions and arriving at a conclusion. 5:09:46 Hypothesis testing involves two stages: formulating the hypothesis and performing the test. 5:15:24 Understanding hypothesis testing and conclusion 5:18:14 Understanding the importance of uncertainity in accepting or rejecting null in statistics 5:23:20 Probability and null hypothesis rules 5:26:04 Formulating alternate hypothesis is crucial for hypothesis testing 5:30:38 Types of Hypothesis Testing 5:33:47 Understanding the application of Z test and T test in hypothesis testing. 5:39:07 Performing the test for new sample mean 5:41:45 Understanding rejection regions and significance level in hypothesis testing. 5:47:29 Reject or accept null hypothesis based on sample mean 5:49:46 Formulating hypothesis and finding critical values 5:55:14 Hypothesis testing for population mean 5:58:07 Reject null with increased status score due to sample uncertainty 6:03:54 T-Test example with loan maintenance cost 6:06:18 Understanding T critical and T estimated in lower tail test 6:11:43 Explanation of one sample T Test 6:14:38 Analyzing difference to determine program effectiveness 6:20:15 Comparing effectiveness of two different study techniques with independent sample t-test. 6:22:46 Calculating T critical for two tail test 6:28:19 Understanding errors in hypothesis testing is crucial. 6:30:56 Understanding different scenarios in hypothesis testing 6:36:50 Testing hypotheses in statistics 6:40:07 Type 2 error is problematic for government as it may lead to unnecessary construction 6:45:11 Type one and type two errors in statistics 6:48:13 Balancing water supply interruption in hotels
This video is by far the best one among many where u can learn prob and stats for data science. I guess no other tutor has this much clarity of thought. Take a bow for this handsome
Wow! Amazing...It was indeed a treat waching such a beautifully planned session. Kudos to the instructor who actually showed his passion in imparting his knowledge, the editors who did the flawless editing, the camera persons who captured such a stunning video shots and the audio quality. All in all it is a learning lesson for all EDTECH companies. Wonderful 🎉🎉🎉🎉🎉 - How do I access the PPT or notes?
At 05:53:53, Sumit mistakenly stated that the values fall within the acceptance region. Since 34325 is less than 34917, the value will fall within the rejection region, and thus, we will reject the null hypothesis. We hope that helps. Thank You!
00:05 Statistics is broadly classified into descriptive, inferential, and hypothesis
03:08 Descriptive statistics is used to summarize and describe data in a concise manner
09:28 Finding the average study hours in 12th class
12:15 The practical solution is to take a random sample of students for surveying.
18:18 The video discusses the process of statistically proving claims or statements.
21:46 Understanding the distinction between numerical and categorical variables.
27:42 Educational Qualification and Quantitative Variables
30:43 Income and age are continuous quantitative variables with infinite possible values.
36:18 Descriptive statistics include measures of central tendency and measures of variation.
39:24 Mean or average is a measure of central tendency.
44:37 Identifying and handling outliers in data
47:36 Understanding the concept of median
53:10 Mode is the observation with the highest occurrence in the data.
55:53 Understanding measures of central tendency
1:01:20 Subject A has low variation around the average line, while Subject B has high variation.
1:04:19 Measures of variation describe data scatteredness around the center or average line.
1:10:11 Calculating the average and marking the data points around the center
1:12:54 Understanding distance and average deviation from the center
1:18:32 Understanding variance and standard deviation in statistics
1:21:28 Standard deviation shows data spread from the mean.
1:27:05 Standard deviation alone cannot compare variation, use coefficient of variation.
1:29:54 Comparing variation using coefficient of variation
1:35:50 Product number two is very stable in the market
1:38:03 Descriptive statistics helps to explain and conclude data using various measures.
1:43:25 Understanding leverage data point and probability
1:45:55 Uncertainty in random experiments
1:51:25 Understanding equally likely outcomes in sample space.
1:53:51 Probability is influenced by equally likely sample spaces
1:59:20 Sum of all probabilities over the sample space is always equal to 1
2:02:01 Probability of events A and B and their union
2:07:11 Understanding the structure of playing cards and calculating probabilities.
2:09:58 Calculating probability of getting specific cards from a deck
2:15:28 Understanding probability rules and their applications.
2:18:16 Probability Rule: Probability of an event and its complement add up to 1
2:23:20 First toss outcome doesn't affect second toss probability.
2:25:54 Understanding the Gambler's Fallacy
2:31:20 Probability calculation for multiple draws
2:34:06 Probability of dependent events
2:39:57 Random variables quantify the outcome of an experiment.
2:43:03 Random variables can be discrete or continuous.
2:48:59 Continuous random variables can take endless values and are known as The Continuous random variable.
2:51:31 Calculating the Expected Value of a Random Variable
2:57:14 Understanding expected value in probability
2:59:42 Understanding distributions for discrete random variables
3:05:18 Probability of obtaining heads from coin tosses
3:08:03 Observing and categorizing data for random variables
3:13:18 Probability density function deals with ranges, not specific points
3:15:49 Standard discrete and continuous distributions are important in statistics.
3:21:38 Calculating the probability of a binomial experiment
3:24:26 Conducting the experiment to find the probability of getting exactly two orange balls in three trials.
3:29:38 Calculating probability for binomial experiments
3:32:33 Understanding probability and binomial experiments.
3:37:47 Calculating the probability using combination formula and plotting the probabilities on a column chart.
3:40:35 Probability of hitting at least 5 times is 36%
3:45:41 Standard Normal Distribution and Z-scale
3:48:13 Scaling is the process of converting data into a common scale for comparison.
3:53:07 Calculating minimum and maximum values for different columns
3:56:28 Understanding the normalized scale for values close to zero and one
4:02:24 Z scaling converts data to mean zero and standard deviation one unit.
4:04:41 Standard normal distribution properties explained.
4:09:21 The data distribution is close to standard normal distribution but not exactly the same.
4:11:58 Calculating percentages and proving properties of standard deviation
4:17:41 Understanding Z table for finding cumulative probability
4:20:33 Analyzing the probability of data points within specific ranges.
4:26:19 Using Z tables to find probability and Z scores
4:28:51 Analyzing normal distribution and probability calculation
4:34:20 Right skewness is caused by outliers, affecting mean position.
4:37:13 Normal distribution properties and their applicability
4:42:35 Survey is essential for data collection.
4:45:13 Using sample statistic to estimate population mean
4:50:55 Central Limit Theorem properties
4:53:38 Sampling distribution closely resembles a normal distribution
4:58:39 Estimating population parameter range from sample mean
00:01 Understanding the significance of range and confidence interval in statistics
5:06:50 Hypothesis testing involves two assumptions and arriving at a conclusion.
5:09:46 Hypothesis testing involves two stages: formulating the hypothesis and performing the test.
5:15:24 Understanding hypothesis testing and conclusion
5:18:14 Understanding the importance of uncertainity in accepting or rejecting null in statistics
5:23:20 Probability and null hypothesis rules
5:26:04 Formulating alternate hypothesis is crucial for hypothesis testing
5:30:38 Types of Hypothesis Testing
5:33:47 Understanding the application of Z test and T test in hypothesis testing.
5:39:07 Performing the test for new sample mean
5:41:45 Understanding rejection regions and significance level in hypothesis testing.
5:47:29 Reject or accept null hypothesis based on sample mean
5:49:46 Formulating hypothesis and finding critical values
5:55:14 Hypothesis testing for population mean
5:58:07 Reject null with increased status score due to sample uncertainty
6:03:54 T-Test example with loan maintenance cost
6:06:18 Understanding T critical and T estimated in lower tail test
6:11:43 Explanation of one sample T Test
6:14:38 Analyzing difference to determine program effectiveness
6:20:15 Comparing effectiveness of two different study techniques with independent sample t-test.
6:22:46 Calculating T critical for two tail test
6:28:19 Understanding errors in hypothesis testing is crucial.
6:30:56 Understanding different scenarios in hypothesis testing
6:36:50 Testing hypotheses in statistics
6:40:07 Type 2 error is problematic for government as it may lead to unnecessary construction
6:45:11 Type one and type two errors in statistics
6:48:13 Balancing water supply interruption in hotels
Thank you so much for your indexing ❤
It's finally done. Thank you so much for your easy and fluent lecture brother.
Love from Bangladesh
Thank you for teaching us with so much dedication and patience. It's very rare to see such teachings in today's era
At 5:53:25 the null hypothesis should be rejected at 34,325 is less than 34,917 and therefore falls in the rejected area. Someone please confirm.
Yes it should be rejected there were some misunderstandings.
Yes Ha falls in rejected area
You are correct, he made a mistake there.
Jup. True :). Was also confused for a minute :).
Actually ,this video deserves more views...
at 6:07:50, the value of T-estimated is coming out as -0.58, and not -1.30.
at 6:24:55. the value of df should be 28, not 27
This video is by far the best one among many where u can learn prob and stats for data science. I guess no other tutor has this much clarity of thought. Take a bow for this handsome
Happy to hear that! 🙌🏼
Awesome Lecture
Wow! Amazing...It was indeed a treat waching such a beautifully planned session. Kudos to the instructor who actually showed his passion in imparting his knowledge, the editors who did the flawless editing, the camera persons who captured such a stunning video shots and the audio quality. All in all it is a learning lesson for all EDTECH companies. Wonderful 🎉🎉🎉🎉🎉
- How do I access the PPT or notes?
Thanks! Keep an eye out for more such videos! 😃
please provide the remaining math's concept like algebra and calculus its very helpful for us
Learn more about Scaler: bit.ly/47CkpBD
Sir I love your teaching can you make video in Hindi which will be more helpful for us😊
isnt Coefficient of variation = Standard deviation / mean.
I think median has been used here. Is that a mistake or can we use median too?
50% of this was already taught to us in school , pr hme to satiabaaji krni thi 🤧😃
Satiabaaji means 🤔
hahahahahahahhahhahaha maza Aagya
This video is really good! How to access the notes?
calculus is required for this problm??
Can we get this as ppt
IQR explanation is missing,
I like it
Is it enough for learning?
Most of the important concepts are already covered in this video for a beginner level Data Scientist! Happy Learning
Is this cover everything need for data science?
Not exactly.
Williams Thomas Moore Steven Hernandez David
Lee Margaret Williams Sandra Hall Gary
Taylor Nancy Brown Barbara Young Carol