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PSN Academy
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Computer Science, Data Science, Artificial Intelligence and Machine Learning, Statistics
Standard Error and Correction factor in Statistics | Sampling distribution of sample means
Standard Error: The standard deviation of sampling distribution of a statistic from the population parameter is known as Standard Error, denoted by SE.
Standard errors have been calculated based on the following assumption: Sampling done from an infinite population, OR from a finite population with replacement.
Correction factors for Standard Error
Correction to the calculations of standard errors mentioned is required when:
1. the population is not large in relation to the sample size i.e., 𝑁 less than 10𝑛, and
2. sampling is done without replacement.
Correction is done by multiplying the standard errors by the correction factor.
The process of projecting the sample results for the whole population is
known as statistical inference.
The standard error is used to express the accuracy or precision of the estimate
of population parameter because the reciprocal of the standard error is the
measure of reliability or precision of the statistic. Standard error also
determines the probable limits or confidence limits within which the population parameter may be expected to lie with
certain level of confidence. Standard error is also applicable in testing of
hypothesis.
Standard error is inversely
proportional to the sample size. Therefore, as sample size increases the
standard error decreases.
When the parent population is normal
then all the sampling distributions for varying sample sizes are also normal
whereas when parent population is uniform, binomial, exponential then the
shapes of the sampling distributions of mean are not in the form of specify
distribution when sample size is small.
Generally, when samples are drawn non-normal populations then it is not
possible to specify the shape of the sampling distribution of mean when the
sample size is small. Although when sample size is large (more than 30) then we
observed that sampling distribution of mean converges to normal distribution
whatever the form of the population i.e. normal or non-normal.
After knowing the shapes of the sampling distribution of mean in different
situations, the mean and variance of the
sampling distribution of mean can be calculated when samples are drawn from normal population.
----------------------------------------
Chapters:
00:00 What is Sampling Distribution?
01:04 Example of sampling distribution
06:36 Why is the mean of sample means equal to the population mean?
09:37 How to calculate the mean of sample means?
10:52 How to calculate the expectation of sample means?
11:35 Is the distribution type of population same as that of samples?
12:23 What is the need of sampling?
13:40 What is Standard Error?
15:40 Example of calculating standard error of sample mean
17:08 Assumptions for calculating standard error
17:40 Standard error formula for sample statistics
19:07 Why standard error decreases as sample count increases?
19:30 When are correction factors for standard error required?
21:26 Numerical example for Standard error for proportion
26:13 Calculation of Confidence Interval for sample proportion
27:09 Z scale in Normal distribution
30:59 Numerical example for Standard error between two samples
#psnacademy #samplingdistribution #standarderror #zscore #normaldistribution #confidenceinterval #correctionfactor
Standard errors have been calculated based on the following assumption: Sampling done from an infinite population, OR from a finite population with replacement.
Correction factors for Standard Error
Correction to the calculations of standard errors mentioned is required when:
1. the population is not large in relation to the sample size i.e., 𝑁 less than 10𝑛, and
2. sampling is done without replacement.
Correction is done by multiplying the standard errors by the correction factor.
The process of projecting the sample results for the whole population is
known as statistical inference.
The standard error is used to express the accuracy or precision of the estimate
of population parameter because the reciprocal of the standard error is the
measure of reliability or precision of the statistic. Standard error also
determines the probable limits or confidence limits within which the population parameter may be expected to lie with
certain level of confidence. Standard error is also applicable in testing of
hypothesis.
Standard error is inversely
proportional to the sample size. Therefore, as sample size increases the
standard error decreases.
When the parent population is normal
then all the sampling distributions for varying sample sizes are also normal
whereas when parent population is uniform, binomial, exponential then the
shapes of the sampling distributions of mean are not in the form of specify
distribution when sample size is small.
Generally, when samples are drawn non-normal populations then it is not
possible to specify the shape of the sampling distribution of mean when the
sample size is small. Although when sample size is large (more than 30) then we
observed that sampling distribution of mean converges to normal distribution
whatever the form of the population i.e. normal or non-normal.
After knowing the shapes of the sampling distribution of mean in different
situations, the mean and variance of the
sampling distribution of mean can be calculated when samples are drawn from normal population.
----------------------------------------
Chapters:
00:00 What is Sampling Distribution?
01:04 Example of sampling distribution
06:36 Why is the mean of sample means equal to the population mean?
09:37 How to calculate the mean of sample means?
10:52 How to calculate the expectation of sample means?
11:35 Is the distribution type of population same as that of samples?
12:23 What is the need of sampling?
13:40 What is Standard Error?
15:40 Example of calculating standard error of sample mean
17:08 Assumptions for calculating standard error
17:40 Standard error formula for sample statistics
19:07 Why standard error decreases as sample count increases?
19:30 When are correction factors for standard error required?
21:26 Numerical example for Standard error for proportion
26:13 Calculation of Confidence Interval for sample proportion
27:09 Z scale in Normal distribution
30:59 Numerical example for Standard error between two samples
#psnacademy #samplingdistribution #standarderror #zscore #normaldistribution #confidenceinterval #correctionfactor
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Відео
![Important concepts on Statistics for Data Science interviews [Part 2 of 4] | PSN Academy](http://i.ytimg.com/vi/WaHmu6s073M/mqdefault.jpg)
![Important concepts on Statistics for Data Science interviews [Part 2 of 4] | PSN Academy](/img/tr.png)
Important concepts on Statistics for Data Science interviews [Part 2 of 4] | PSN Academy
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11. Bias: Assumptions made by a predictive algorithm. The error occurred due to bias is called Bias error or Error due to bias. Low Bias algorithms: k-NN, SVM, Decision trees High Bias algorithms: Linear regression, Linear Discriminant Analysis (LDA), Logistic regression Variance: Amount of change in estimate by the learning algorithm 𝐿 on changed training dataset. Difference: Too large: Model ...
Kolmogorov-Smirnov test (K-S test) - Non parametric - One sample test | PSN Academy
Переглядів 8418 місяців тому
Kolmogorov-Smirnov test (KS test) measures the goodness of fit of an observed data (also called empirical data) to a theoretical distribution for testing whether data follow a specified or assumed distribution or sample has come from a specified or assumed distribution or there is a significant difference between an observed distribution and a theoretical distribution. Assumptions (i) The sampl...
R Squared - Proportion of explained variation in the target variable | PSN Academy #shorts
Переглядів 178 місяців тому
#shorts R Squared: Proportion of explained variation in the target variable. It measures the degree of variability in the target variable explained by the independent variables. 𝑅^2="Explained variation" /"Total variation" Independent variables are responsible for Explained variation.
Maximum likelihood estimation of Linear Regression | PSN Academy
Переглядів 768 місяців тому
Finds the best values of parameters for which the model has the best fit i.e. optimized. This finding procedure is based on Probability theory. You may argue that we can determine the 𝜇 and 𝜎 directly from the dataset. What’s the point of trial and error? Well that’s true in this case. But there are situations where a direct method of determining the parameter values is not available. In those ...
![Important concepts on Statistics for Data Science interviews [Part 1 of 4] | PSN Academy](http://i.ytimg.com/vi/SBgQE8g7nYM/mqdefault.jpg)
Important concepts on Statistics for Data Science interviews [Part 1 of 4] | PSN Academy
Переглядів 358 місяців тому
1. P-value: Probablity of "By chance" of getting such data indicating effect between variables. 2. Linear Regression: Used to determine the function definition between one or more independent (Predictor) variables and one dependent (Target) variable. If the curve of the function is a straight line, then the line is Line of regression and the regression is said to be Linear regression. 3. Simpso...
Bonferroni Correction | Post-Hoc Followup test | Multiple Contrasts or Comparisons | PSN Academy
Переглядів 709 місяців тому
Planned Contrasts: These are tests for hypotheses that were posed before conducting hypothesis test. Post hoc Contrasts: These are tests for hypotheses that did not appear in the original analysis plan. These are hypotheses posed after data collection and analysis. Step 1: Perform test (e.g. ANOVA). Step 2: If Null hypothesis is rejected, define Null hypothesis for Follow-up tests. Step 3: Pefo...
One-way ANOVA | Complete Statistical analysis from Hypothesis to Decision-making | PSN Academy
Переглядів 349 місяців тому
It is a tester of Null Hypothesis. ANOVA stands for Analysis Of Variance used for studying cause-and-effect of one or more factors on a single dependent variable. The independent variables MUST BE of nominal scale (categorical) and the dependent variable MUST BE metric (continuous). Step 1: Setup Null hypothesis. Step 2: Calculate the Variation_Between and Variation_Within of the samples. Step ...
![Chi-Square Test | Complete roadmap [with example] from Hypothesis to Interpretation | PSN Academy](/img/n.gif)
Chi-Square Test | Complete roadmap [with example] from Hypothesis to Interpretation | PSN Academy
Переглядів 269 місяців тому
It is a tester of Null Hypothesis. Two cases where Chi-square test is applicable: 1. Determines whether there is a significant ASSOCIATION between TWO CATEGORICAL variables 2. Determines Goodness of Fit test Step 1: Setup Null hypothesis. Step 2: Calculate the Expected frequency for each observed data. Step 3: Calculate the Chi-square score of the given data. Step 4: Calculate the degrees of fr...
Is Median always equal to 50th Percentile?
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Percentile of a number 𝑥 in an ordered list of numbers is the number count less than 𝑥 in percentage form. A few words on percentile The concept of Percentile is controversial. The median is not always equal to 50th percentile. There are several methods for calculating percentile of a number. Please read the discussions on math.stackexchange.com/, quora.com or similar platforms. onlinestatbook....
Kelly’s Coefficient of Skewness
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Kelly’s Coefficient of Skewness uses the concpet of percentile. Kelly’s Coefficient of Skewness 𝑆_𝑘 = ((𝑃_90−𝑃_50 )−(𝑃_50−𝑃_10 ))/((𝑃_90−𝑃_10 ) ) For a symmetrical distribution, 𝑆_𝑘=0. Mean greater than Mode (Positively skewed): S_k is Positive Mean less than Mode (Negatively skewed): S_k is Negative For moderately skewed distribution: S_k lies between -1 snd 1. Percentile of a number 𝑥 in an o...
Bowley's Coefficient of Skewness
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Bowley's Coefficient of Skewness uses the concpet of quartile. Bowley's Coefficient of Skewness 𝑆_𝑘 = ((𝑄_3−𝑄_2 )−(𝑄_2−𝑄_1 ))/((𝑄_3−𝑄_1 ) ) For a symmetrical distribution, 𝑆_𝑘=0. Mean greater than Mode (Positively skewed): S_k is Positive Mean less than Mode (Negatively skewed): S_k is Negative For moderately skewed distribution: S_k lies between -1 snd 1. A few words on percentile The concept ...
𝐊𝐚𝐫𝐥 𝐏𝐞𝐚𝐫𝐬𝐨𝐧’𝐬 𝐂𝐨𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐨𝐟 𝐒𝐤𝐞𝐰𝐧𝐞𝐬𝐬
Переглядів 227Рік тому
Coefficient of Skewness 𝑆_𝑘 = (𝑀𝑒𝑎𝑛−𝑀𝑜𝑑𝑒)/𝑆𝐷 For a symmetrical distribution, 𝑆_𝑘=0. Mean greater than Mode (Positively skewed): S_k is Positive Mean less than Mode (Negatively skewed): S_k is Negative For moderately skewed distribution: 〖−1≤𝑆〗_𝑘≤ 1 If mode is not well defined, then it will be difficult to estimate it. The empirical relation for determining mode: 𝑥 ̅−𝑀𝑜 = 3(𝑥 ̅−𝑀𝑖) 𝑆_𝑘 = (𝑥 ̅−𝑀𝑜...
𝜷 and 𝜸 coefficient of skewness
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Karl Pearson defined the following 𝛽 and 𝛾 coefficients of skewness, based upon the central moments of order 2 and 3: For a symmetrical distribution, 𝛽_1=0. Tells about the magnitude of the skewness but not its direction. Karl Pearson’s Gamma coefficient 𝛾_1 𝛾_1=±√(𝛽_1 )=𝑚_3/√((𝑚_2 )^3 )=𝑚_3/(√(𝑚_2 ))^3 =𝑚_3/(√(𝜎^2 ))^3 =𝑚_3/(𝜎)^3 Now the sign of skewness would depend upon the value of 𝑚_3. Ske...
Charlier’s Checks for Moments - clearly explained
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Charlier’s Checks for Moments - clearly explained
Sheppard’s Corrections for Moments - why this is needed?
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Sheppard’s Corrections for Moments - why this is needed?
Effect of Change of Origin and Scale on Moments
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Effect of Change of Origin and Scale on Moments
Relation between moments about Mean and Arbitrary point
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Relation between moments about Mean and Arbitrary point
Moments in statistics | Moments about Mean, Arbitrary point | Deviation | Variance
Переглядів 216Рік тому
Moments in statistics | Moments about Mean, Arbitrary point | Deviation | Variance
Probability estimate: Classical & Frequency approach | Law of large numbers | Event | Sample Space
Переглядів 47Рік тому
Probability estimate: Classical & Frequency approach | Law of large numbers | Event | Sample Space
How to Identify NULL and Research Hypothesis | z-score and t-score | Sample and Population variance
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How to Identify NULL and Research Hypothesis | z-score and t-score | Sample and Population variance
The distinction between Repeated measures and Replicate with example
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The distinction between Repeated measures and Replicate with example
What is an Outcome in an experiment? What relation does it have with Random variable?
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What is an Outcome in an experiment? What relation does it have with Random variable?
5.2.1.2.2 Are Factor and Explanatory variable same? What is a Level & a Treatment in a dataset?
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5.2.1.2.2 Are Factor and Explanatory variable same? What is a Level & a Treatment in a dataset?
5.2.1.2.1 What is the difference between Parameter & Statistic? | Notations of parameter & statistic
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5.2.1.2.1 What is the difference between Parameter & Statistic? | Notations of parameter & statistic
5.2.1.1 What is ANOVA? | Variation & Variance | Within & Between groups | Assignable & Chance causes
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5.2.1.1 What is ANOVA? | Variation & Variance | Within & Between groups | Assignable & Chance causes
Permutation example [Correction: Exclude r! in the denominator]
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Permutation example [Correction: Exclude r! in the denominator]
2.1.1.2 What is Central Tendency? | How to compute Median & Mode from Grouped & Ungrouped data
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2.1.1.2 What is Central Tendency? | How to compute Median & Mode from Grouped & Ungrouped data
Thank youuuu
Very good explanation by you ❣
Make full playlist data structure and algorithm
Thanks for the entire playlist...
Very good explain and and visual representation also help to student to better understand data structure and algorithm
BINGO! sometimes just saying something a certain way makes everything clear. Cheers
great explaination!!
Sir, your tutorials are great thank you. Can you suggest some books that have in depth explanations on these topics.
1. Introduction to Mathematical Statistics - Robert V. Hogg, Joeseph McKean, Allen T Craig 2. OpenIntro Statistics - David Diez, Mine Cetinkaya-Rundel, Christopher D Barr 3. Fundamentals of Statistics Vol 1 & 2 by Goon, Gupta, Dasgupta 4. Schaum's Outline - Probability and Statistics 5. IGNOU study materials (free from IGNOU's official website)
@@PSNAcademy Thank you Sir.
superbly explained. Thank you sir. Will these corrections not apply for skewed distributions ? Also, if class frequency is less than 1000, will these corrections not apply ?
Answer 1: Yes, it is useful for skewed distributions. Answer 2: Yes, these corrections will apply but the corrections will be comparatively less perfect.
@@PSNAcademy got it sir thank you
Hi Sir, very well explained. Can "h" be any number ?
Yes, it will be according to your scaling requirement as in our example (h=1000) in the video.
@@PSNAcademy thank you
Very good video 😀
Make a vedio on hypergeometric distribution
Why we take half of the random variable ? Can you explain more details with another example?
It is not half of the r.v., you are still thinking of it as a conventional variable - actually it is a function. A third example may be "X = Tail count squared" - that is, we count the Tails and square it. The definitions of r.v. depends on the requirements of a particular experiment.
It's very easy to understand for students and it's very helpful for themselves
Hi Learners! This video is a part of the series on Applied Statistics. I hope you are benefited after seeing this video. Do write in the comment section if you have query. Also it will be my pleasure to hear from you anything you dislike in the video playback so that I'll rectify those in the future videos. After all, I'm not professional in video production.😊 --- You may watch my other videos: 1. Factorial and Counting techniques - ua-cam.com/video/AAyoM0tvjUY/v-deo.html 2. Permutation and Combination - ua-cam.com/video/14UbiMaBqEE/v-deo.html 3. What is ANOVA? | Variation & Variance | Within & Between groups | Assignable & Chance causes ua-cam.com/video/GSqtieJBrec/v-deo.html *Playlists* 1. Playlist for Linked List: ua-cam.com/video/G666OSppPMc/v-deo.html 2. Playlist for Queue: ua-cam.com/video/_rdvS4X6_xM/v-deo.html 3. Playlist for Stack: ua-cam.com/video/U-nP6HJYZYY/v-deo.html 4. Playlist for Tree: ua-cam.com/video/4n972w7JI2M/v-deo.html
Good information Thank you sir
Hi Learners! Is the picture regarding Permutation and Combination clearer to you now? Please let me know in the comment section. Also it will be my pleasure to hear from you anything you dislike in the video playback. --- You may watch my other videos: 1. Factorial and Counting techniques - ua-cam.com/video/AAyoM0tvjUY/v-deo.html 2. Permutation and Combination - ua-cam.com/video/14UbiMaBqEE/v-deo.html 2. Playlist for Linked List: ua-cam.com/video/G666OSppPMc/v-deo.html 3. Playlist for Queue: ua-cam.com/video/_rdvS4X6_xM/v-deo.html 4. Playlist for Stack: ua-cam.com/video/U-nP6HJYZYY/v-deo.html 5. Playlist for Tree: ua-cam.com/video/4n972w7JI2M/v-deo.html
Hi Learners! Is the picture regarding Permutation and Combination clearer to you now? Please let me know in the comment section. Also it will be my pleasure to hear from you anything you dislike in the video playback. --- You may watch my other videos on data structure: 1. Factorial and Counting techniques - ua-cam.com/video/AAyoM0tvjUY/v-deo.html 2. Playlist for Linked List: ua-cam.com/video/G666OSppPMc/v-deo.html 3. Playlist for Queue: ua-cam.com/video/_rdvS4X6_xM/v-deo.html 4. Playlist for Stack: ua-cam.com/video/U-nP6HJYZYY/v-deo.html 5. Playlist for Tree: ua-cam.com/video/4n972w7JI2M/v-deo.html
Hi Learners! Is the picture regarding Factorial and Principles of Counting clearer to you now? Please let me know in the comment section. Also it will be my pleasure to hear from you anything you dislike in the video playback. --- You may watch my other videos on data structure: 1. Playlist for Linked List: ua-cam.com/video/G666OSppPMc/v-deo.html 2. Playlist for Queue: ua-cam.com/video/_rdvS4X6_xM/v-deo.html 3. Playlist for Stack: ua-cam.com/video/U-nP6HJYZYY/v-deo.html 4. Playlist for Tree: ua-cam.com/video/4n972w7JI2M/v-deo.html
It is clear now to me. thank you.
I need a program on this on python.
Nice explanation!!
Hi Learners! Are you comfortable with Quicksort and its complexity calculation after watching my video? If you need any clarification regarding this topic, write in the comment section. I'll respond to you. You may watch my other videos on data structure: 1. Playlist for Linked List: ua-cam.com/video/G666OSppPMc/v-deo.html 2. Playlist for Queue: ua-cam.com/video/_rdvS4X6_xM/v-deo.html 3. Playlist for Stack: ua-cam.com/video/U-nP6HJYZYY/v-deo.html 4. Playlist for Tree: ua-cam.com/video/4n972w7JI2M/v-deo.html
Hi Learners! Please let me know whether you can understand my explanation. If you need any clarification regarding this topic, do not hesitate to write in the comment section. I'm eager to hear from you. PRIORITY QUEUE | Introduction with example: ua-cam.com/video/R9hxLqJ0hh0/v-deo.html PRIORITY QUEUE | Array representation - the idea: ua-cam.com/video/wam-8aiVmhg/v-deo.html
Hi Learners! Please let me know whether you can understand my explanation. If you need any clarification regarding this topic, do not hesitate to write in the comment section. I'm eager to hear from you. LINKED LIST [DOUBLY]: Using XOR - The working principle | Data Structure Full Course: ua-cam.com/video/ciMNPA301r0/v-deo.html QUEUE: Introduction [ANIMATED] with example: ua-cam.com/video/_rdvS4X6_xM/v-deo.html STACK: Concept with example | Pop and Push operations: ua-cam.com/video/U-nP6HJYZYY/v-deo.html LINKED LIST: An introduction to Doubly linked list: ua-cam.com/video/R16AC49xwWA/v-deo.html LINKED LIST: What is it? | Why should I learn it?: ua-cam.com/video/G666OSppPMc/v-deo.html
Hi Learners! Please let me know whether you can understand my explanation. If you need any clarification regarding this topic, do not hesitate to write in the comment section. I'm eager to hear from you. You may watch my previous videos on Priority Queue: PRIORITY QUEUE | Introduction with example: ua-cam.com/video/R9hxLqJ0hh0/v-deo.html PRIORITY QUEUE using Linked List | Insert & Delete item: ua-cam.com/video/qGBbUeRE7O4/v-deo.html PRIORITY QUEUE | Array representation - the idea: ua-cam.com/video/wam-8aiVmhg/v-deo.html PRIORITY QUEUE using array | How to insert item: ua-cam.com/video/dd7iee9Sw7w/v-deo.html
Hi Learners! Please let me know whether you can understand my explanation. If you need any clarification regarding this topic, do not hesitate to write in the comment section. I'm eager to hear from you. In case you do not have the idea what a Binary Tree is, please watch this: ua-cam.com/video/4n972w7JI2M/v-deo.html
Hi Learners! Please let me know whether you can understand my explanation. If you need any clarification regarding this topic, do not hesitate to write in the comment section.😊 In case you missed these topics: LINKED LIST: An introduction to Doubly linked list ua-cam.com/video/R16AC49xwWA/v-deo.html
Hi Learners! Please let me know whether you can understand my explanation. If you need any clarification regarding this topic, do not hesitate to write in the comment section.😊 1. LINKED LIST: What is it? | Why should I learn it? ua-cam.com/video/G666OSppPMc/v-deo.html 2. LINKED LIST: How it looks like in memory ua-cam.com/video/YfwZxAago7k/v-deo.html 3. LINKED LIST: Four types of pointers used ua-cam.com/video/Kkz2lByUgbA/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, do not hesitate to write in the comment section.😊 In case you missed the introduction to Singly Linked List: ua-cam.com/video/YfwZxAago7k/v-deo.html In case you missed the introduction to Doubly Linked List: ua-cam.com/video/R16AC49xwWA/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on LINKED LIST [DOUBLY]: Using XOR - The working principle: ua-cam.com/video/ciMNPA301r0/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on Preorder traversal Algorithm ua-cam.com/video/O5M6zfXGn6Q/v-deo.html Binary Tree: How to create | Code in C explained: ua-cam.com/video/kfrUwAys_2Y/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on Preorder traversal Algorithm | Code in C explained: ua-cam.com/video/H2kyJil-Who/v-deo.html Binary Tree: How to create | Code in C explained: ua-cam.com/video/kfrUwAys_2Y/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on Preorder traversal Algorithm | Code in C explained: ua-cam.com/video/H2kyJil-Who/v-deo.html Binary Tree: How to create | Code in C explained: ua-cam.com/video/kfrUwAys_2Y/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on Inorder traversal: tua-cam.com/video/c5BeycqamL4/v-deo.html Implementation of Inorder traversal algorithm in C: ua-cam.com/video/SMdD2unNlfE/v-deo.html Binary Tree : Preorder traversal Algorithm | Code in C explained: ua-cam.com/video/H2kyJil-Who/v-deo.html Binary Tree: How to create | Code in C explained: ua-cam.com/video/kfrUwAys_2Y/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on Inorder traversal: ytua-cam.com/video/c5BeycqamL4/v-deo.html Implementation of Inorder traversal algorithm in C: ua-cam.com/video/SMdD2unNlfE/v-deo.html Binary Tree : Preorder traversal Algorithm | Code in C explained: ua-cam.com/video/H2kyJil-Who/v-deo.html Binary Tree: How to create | Code in C explained: ua-cam.com/video/kfrUwAys_2Y/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, address me in the comment section.😊 In case you missed the video on Inorder traversal: ua-cam.com/video/c5BeycqamL4/v-deo.html Implementation of Inorder traversal algorithm in C: ua-cam.com/video/SMdD2unNlfE/v-deo.html
Hi Learners! Please let me know how my explanation goes. If you need any clarification regarding this topic, do not hesitate to write in the comment section.😊 In case you do not have the idea what is a Binary Search Tree? ua-cam.com/video/rstQHT3y98k/v-deo.html
All examples are different and very interesting for learning
Thank you! 😃
It's very easy to understand for students Thank you sir
All videos are very helpful for students and myself so sir I want say please make more videos. Thank you
Make more vedio sir
Sure I will.
Very good explanation it's very helpful for students
Glad to hear that!
Content delivered in a very detailed manner !!💯
Glad you think so!
0.08×4+0.4×1+0.25×2+0.15×3+0.12×4=2.15
Keep doing more videos
Thank u.It helped me a lot
Need more videos on Slope
sir what if after adding first 2 the result is equal to next value in the sequence
You can place them in any order.
thanks for providing containts.