Ask Ghassem - Recent activity in Data Science

Answered: How to analyse imbalanced categorical colum in dataset

Thu, 19 Feb 2026 18:40:15 +0000

For imbalanced categorical data, you shouldn't drop the column. Instead, you can try techniques like oversampling the minority class or using models that handle imbalanced data well, like XGBoost. This way, you can still extract useful insights without losing important information.

Can you verify the validity of this chart comparing the review scores for Marvel Phase 4?

Mon, 09 Jan 2023 16:29:14 +0000

I have some skepticism about the validity of the charts below comparing the critic and audience reviews for Phase 4 of the MCU to the previous 3 phases. There are over 18 movies and tv shows in Phase 4 compared to the 6 movies in Phases 1 & 2 and the 11 movies in Phase 3. Also, there are far fewer critic reviews for the Phase 4 tv shows than the Phase 4 movies. For example, on Rotten Tomatoes there are only 40 critic reviews for The Falcon and the Winter Soldier and 452 critic reviews for Black Widow. Could this uneven and inconsistent number of reviews between tv shows and movies in Phase 4 be inaccurately making the overall averages higher than they should be? Or do you agree with the conclusions presented in the charts?

https://cdn.discordapp.com/attachments/997145183172964435/1059948060194652230/image.png

https://cdn.discordapp.com/attachments/997145183172964435/1049356020469739520/image.png

How do I compare the count of a value in each year while having a different sanple size each year.

Wed, 08 Jun 2022 10:32:33 +0000

How do I accurately compare between the number of something a survey measure from my employees each year with a varying umber of survey engagement and employee size?

If I was measuring the satisfaction of my employees over the years by collecting a survey from my them each year by asking them wether they are satisfied or not, and then comparing yes’s over the years but the number of employees who answer is not the same each year and the number of employees increases every year. How do I correctly compare this throughout each year?

In other words, how do I remove the effect of the survey engagement rate when calculating the results?

Is it possible to make a forecast of a future value of Air Temperature using Fast Fourier Transform?

Thu, 02 Jun 2022 16:10:26 +0000

Is it possible to make a forecast of a future value of Air Temperature using Fast Fourier Transform, if yes, what should be the process or how you'll be able to do it. Thank you!

forecast log transformed fitted values for 2 years using ARMA model

Wed, 04 May 2022 20:31:44 +0000

Input is a stock price in exponential transformation. We are asked to forecast using ARMA results for 2 years.

Recategorized: Battery data projects

Thu, 03 Mar 2022 09:42:53 +0000

Where can I find projects related to battery data?

How can you build dynamic pricing model with data only from rigid pricing?

Fri, 21 Jan 2022 06:44:31 +0000

I want to build a dynamic pricing model which means if product is too expansive for a client and there is a risk that we might loose a client we lower the price for them but if client doesn't care that much about the price we might increase price a little.

All the articles I've seen describe some kind of A/B testing for the pricing and then create a model.

I want to build a model only on the existing rigid pricing data. So I have prices offered to customers and I know who bought the product and who went to other company.

How can I do the increasing price part?

What analytical software would be good for a company to use?

Fri, 14 Jan 2022 16:46:38 +0000

This would be for a company that is just now looking into using a software to track data for wine making.

How do I know which encoder to use to convert from categorical variables to numerical?

Mon, 29 Nov 2021 04:09:06 +0000

So say I have a column with categorical data like different styles of temperature: 'Lukewarm', 'Hot', 'Scalding', 'Cold', 'Frostbite',... etc.

I know that we can use pd.get_dummies to convert the column to numerical data within the dataframe, but I also know that there are other 'converters' (not sure if that's the correct terminology) that we can use, i.e. OneHotEncoder from Sk-learn (like I could use the pipeline module to make a nice pipeline and feed my dataframe through the pipeline to also get my categorical data encoded to numerical).

How do I know which to use? Does it matter? If it does matter, when does it matter the most (i.e. what types of problems? When there are lots of categorical variables, or few?) If anyone can give me any pointers on this type of stuff I'd greatly appreciate it.

ValueError: Length mismatch: Expected axis has 60 elements, new values have 2935849 elements

Fri, 26 Nov 2021 06:09:16 +0000

I'm creating a new data frame with the most used items grouped together. But I got the following error when grouping through ID and items. ValueError: Length mismatch: Expected axis has 60 elements, new values have 2935849 elements.

df = sales_df[sales_df['shop_id'].duplicated(keep=False)]
df['Grouped'] = sales_df.groupby('shop_id')['item_name'].transform(lambda x: ','.join(x))
df2 = df[['shop_id', 'Grouped']].drop_duplicates()

In the aforementioned code, I'm making a data frame with respect to shop id and then grouping through shop items. My objective here is to group items with similar ID.

Text Mining, Artificial Neural Networks, Speech Processing, Cloud Computing in DS? Essential for a good Data Scientist ?

Wed, 27 Oct 2021 19:15:16 +0000

Classification of data object might be incorrect

Mon, 25 Oct 2021 15:26:46 +0000

I am learning a new Salesforce product (Evergage) for the company I work for. In the program's documentation they have listed a set of data objects as an example. It appears to me that the classification might be incorrect. Their system makes a division between 'catalog objects' and 'profile objects' and the example they have given is a banking institution. They classified Customer Credit Card as a profile object and Credit Card Level as a catalog object. Seems to me that it should be the other way i.e Customer Credit Card = catalog object and Credit Card Level = profile object. Maybe I am not reading the context correctly?

here is a link to an image with the complete classification: https://drive.google.com/file/d/1nG4aX4Ty_NoHxm04AQo1Ow61m3MZ3pXm/view?usp=sharing

Can Data Science solve this problem?

Sun, 24 Oct 2021 15:43:11 +0000

So, I live in Brazil, and I have a task for college that I don't know what data science method to use, if at all, to solve it. My idea is the following: We Brazilians have Real (BRL) as currency, and we of course have the dollar quotation value to see "how many Reais a dollar is worth". What I wanted to do was to make a research and see whether the Country News have any influence over this price. So for example, if Bolsonaro, our president, says some dumb stuff, the dollar got up in price, and vice versa. What I wanted to do was collect all dollar values and variance over a set time interval, and try and get webscraping to get the news over some economy sites. Here's my question then: How can I correlate the news with the dollar variance over a set time? Can data science do that? How do I preprocess this, if at all? Do I need to use bag-of-words? At least I heard so... Please help and thank you for reading.

should i start as a data analyst then data science?

Mon, 21 Jun 2021 20:31:04 +0000

should I start as a data analyst then data science?

I am a second-year Bachelor's in Computer Science and wanted to pursue to be a Data Scientist.

However, when I am trying to apply for internships/jobs, most of it requires a Masters's/Ph.D.

But, a Data Analyst has fewer requirements.

Do you recommend starting off as a Data Analyst and then change to Data Science?

Answered: How best to ensure data quality?

Fri, 11 Jun 2021 18:09:27 +0000

This is really a broad question. The best quality usually comes from a good quality source of data generation.

Searching for movie dataset containing movie synopses/plots?

Thu, 27 May 2021 09:57:31 +0000

Hello
To build a hybrid recommendation system, I used the movielens 1M dataset, for the collaborative filtering part. Now, I'm looking for a database/dataset that contains descriptions/summaries/details/synopses/plots of movies for the content-based recommendation.
Is there someone who could help me and tell me where I can find a such dataset?
thank you in advance.

Answered: How to calculate average with deviating sensors?

Wed, 05 May 2021 11:49:02 +0000

What seems to work is simple: create 4 new columns:
x=average(3 values) ; y=stdev.p(3 values) ; low threshold = x-y ; high threshold = x+y
, then repeat the numbers if these are within the boundaries and make a 'n/a' if outside of stdev.
With the values repeated (as within thresholds), the average can be calculated neglecting the extreme values.
Example:
10 ; 11 ; 20 : avg=13.67: stdev.p = 4.49; Low=9.17; high=18.16, so
10 ; 11; n/a (as 20 > 18.16)
this gives an average of 10.5 :-)

Agree?

Answer selected: Terminology clarification in Spark

Wed, 17 Feb 2021 16:04:05 +0000

The fact is the engine is still the same, regardless of which interface language you use. For some tasks, such as special cleaning we probably do not have SQL commands, and we have to use Scala or Python. Using Zeppelin, you can switch back and forth among languages the engine supports, however it is not a common practice. For some specific tasks, you can use pure Spark SQL or if you want to use the SQL in pyspark or scala, there are functions that can help you achieve the goal.

I believe observing more examples will help you understand when you can use what.

My GloVe word embeddings contain sentiment?

Sun, 03 Jan 2021 14:09:37 +0000

I've been researching sentiment analysis with word embeddings. I read papers that state that word embeddings ignore sentiment information of the words in the text. One paper states that among the top 10 words that are semantically similar, around 30 percent of words have opposite polarity e.g. happy - sad.

So, I computed word embeddings on my dataset (Amazon reviews) with the GloVe algorithm in R. Then, I looked at the most similar words with cosine similarity and I found that actually every word is sentimentally similar. (E.g. beautiful - lovely - gorgeous - pretty - nice - love). Therefore, I was wondering how this is possible since I expected the opposite from reading several papers. What could be the reason for my findings?

Two of the many papers I read:

Yu, L. C., Wang, J., Lai, K. R. & Zhang, X. (2017). Refining Word Embeddings Using Intensity Scores for Sentiment Analysis. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 26(3), 671-681.
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T. & Qin, B. (2014). Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, 1: Long Papers, 1555-1565

is it possible to derive a new 95% CI from two separate 95% CIs?

Mon, 23 Nov 2020 14:45:19 +0000

Probability of a bus arrived in its destination based on weather condition

Mon, 09 Nov 2020 13:06:47 +0000

A bus is making its way to a destination. If the weather conditions are favorable today, the likelihood of delay is 3%. If the weather conditions are not favorable today, the likelihood of delay is 50%. The forecast predicts that it is 20% likely that the weather conditions will be favorable today.

1. What is the likelihood that the bus will be delayed?

2. The bus has arrived, but it was delayed. Given that the bus was delayed, what is the likelihood that the weather conditions were favorable?

Answered: How to remove unwanted Jupyter notebook kernels?

Fri, 30 Oct 2020 17:18:25 +0000

You can see the list of installed kernels:

jupyter kernelspec list

And then remove those you want with follows (replace the name of the kernel you want to remove in following command)

jupyter kernelspec remove kernel

How can this data be structured for mongodb

Sun, 19 Jul 2020 19:08:50 +0000

https://prnt.sc/tkr2g7 Hello I have a PFE about determining risks of pedestrians, and I have to make a simulator to generate data with something related to this, this is my first time working on this. I would like to know, the structure of data, I will be working with mangodb, so I would love to see an example on JSON

guidance on sequencing data science courses below

Fri, 20 Mar 2020 13:55:49 +0000

Hello
my name is lutaaya mudathiru.

I am planning to start data science online

professional courses at Harvard

University, but i don't know which course i should begin with . I request for help in sequencing these courses below so that i can

benefitt more:

1. Principles, Statistical and Computational Tools for Reproducible Science.

2.Data Science: Inference and Modeling.

3. Data Science: Productivity Tools

4.Data Science: Wrangling

5.Data Science: Linear Regression.

6.Data Science: Machine Learning

7.Data Science: Capstone

8. Data Science: R Basics

9.DataScience:Visualization

10. DataScience:Probability.

11. High-Dimensional Data Analysis

12. Introduction to Linear Models and Matrix Algebra

13. Data science:Statistics and R

14. Fat Chance: Probability from the Ground Up

15. Introduction to Probability (on edX)

Answered: What are the differences among Data Science, Artificial Intelligence and Machine Learning?

Thu, 05 Mar 2020 03:06:12 +0000

This whitepaper contains great insights on each. In a nutshell:

Artificial Intelligence (AI) is a “a huge set of tools for making computers behave intelligently” and in an automated fashion. This includes voice assistants, recommendation systems, and self-driving cars.

Machine Learning (ML) is the “field of study that gives computers the ability to learn without being explicitly programed.” The lion’s share of ML involves computers learning patterns from existing data and applying it to new data in the form of making predictions, such as predicting whether an email is spam or not, whether a customer will churn or not, and diagnosing a particular piece of medical imaging.

Data Science (DS) is about making discoveries and creating insights from data and communicating these insights and discoveries to non-technical stakeholders.

Answered: How to convert Jupyter Notebook or a webpage to PDF using Chrome?

Thu, 27 Feb 2020 18:41:45 +0000

Please take a look at short video below. However it is shown for Google Colab, it can be applied to other document types (Any webpage!)

https://www.youtube.com/watch?v=pNbPQmrfVFY

Answered: How to share a Jupyter Notebook document on Google Colab?

Thu, 27 Feb 2020 16:19:54 +0000

Please watch the following short video which explains sharing Jupyter Notebooks on Google Colab:

https://www.youtube.com/watch?v=oJwe1bxA4jQ

Answered: What are the most common data types in data science?

Wed, 19 Feb 2020 17:33:08 +0000

We can model object attributes in the form of two main forms of Categorical and Numerical. A more detailed explanation is available here.

https://i.imgur.com/JLbmW8m.jpg

Commented: How can I prove that following is a tautology (using laws of logical equivalences)

Tue, 18 Feb 2020 19:50:20 +0000

If you can record a video, and explain step by step, that would be much better. It could be confusing how you conclude line 6 from line 5, but if you just record a video and talk over it while solving, that will be clarified. In addition, I removed your personal information such as student number from your post. Please do not include them while posting publicly.

Answered: can someone send me an online link to discrete mathematics 8th edition textbook?

Thu, 13 Feb 2020 15:27:35 +0000

I think this link is what you want. However, it is better you purchase the book

Answered: How to install Matplotlib

Sat, 08 Feb 2020 06:46:05 +0000

The easiest way is using Anaconda which comes with it pre-installed. You can also use Google Colab.

Understanding symbolic language of problem, quantificational logic

Sun, 26 Jan 2020 11:18:05 +0000

Hi, i am having trouble interpreting the information contained in the relation R, and how it should be applied to the Ps in this problem:

Consider the formula

∃x∃y∃z(P(x,y)∧P(z,y)∧P(x,z)∧¬P(z,x))

Under each pf these interpretations, is this formula true? In each case, R is the relation corresponding to P.

(a) U = N, R = {<x,y> : x<y}.

(b) U = N, R = {<x,x+1> : x≥0}.

Does <x,y> refer to the variables x,y or z in each P(a,b), and the :x<y refer to what the relation between these two should be?

I tried something like this for (a) and got:

∃x∃y∃z((x<y)∧(z<y)∧(x<z)∧¬(z<x))

However I'm not sure if this is correct, and I'm not sure how I would do it for (b)

Answer selected: What are the values stored in tc8 format for the following numbers?

Fri, 24 Jan 2020 01:02:27 +0000

Q1-ANS)
for -117d:

    $-117d = -(01110101)b$

    $-(01110101)b = 10001010(oc8) + 1 = 10001011(tc8)$

for -127d:

    $-127d=-(01111111)b$

    $-(01111111)b = 10000000(oc8) + 1 = 10000001(tc8)$

for 127d:

    $127d = 01111111b = 01111111(tc8)$

for 0d:

    $0d = 00000000b$

    $Two's complement = one's complement + 1 = 11111111(oc8) +1 = 00000000(tc8)$

    due to overflow the 9th digit is removed

Q2-ANS)

    1.

    $10010001(tc8) = -(01101110+1)d = -(01101111)b = -((0 × 2^7) + (1 × 2^6) + (1 × 2^5) + (0 × 2^4) + (1 × 2^3) + (1 × 2^2) + (1 × 2^1) + (1 × 2^0))d = -111d$

    2.

    $00010001(tc8) = ((0 × 2^6) + (0 × 2^5) + (1 × 2^4) + (0 × 2^3) + (0 × 2^2) + (0 × 2^1) + (1 × 2^0))d = 17d$

    3.

    $01111111(tc8) = ((1 × 2^6) + (1 × 2^5) + (1 × 2^4) + (1 × 2^3) + (1 × 2^2) + (1 × 2^1) + (1 × 2^0))d = 127d$

    4.

    $0000000 (tc8) = ((0 × 2^6) + (0 × 2^5) + (0 × 2^4) + (0 × 2^3) + (0 × 2^2) + (0 × 2^1) + (0 × 2^0))d = 0d$

    5.

    $11111111(tc8) = -(00000000 + 1)b = -(00000001) = -1d$

Answer submitted by Amirhossein Azizafshari from Thursday 1 PM class.

Answer selected: How do i find the inverse of the function?

Fri, 06 Dec 2019 04:35:19 +0000

Hey there bro. Let me teach you a trick to solving simple inverse functions. Let's use your question as an example. Let's switch the x and the y. This will give us $x = 4y + 5$. Now try and isolate the y again. You should end up with $y = (x-5)/4$. have fun!

Answer selected: How to calculate $g[h(3)]$ if $g(x)= 2x+3$ and $h(x) =4x+5$ ?

Fri, 06 Dec 2019 04:35:09 +0000

You need to calculate $h(3)$ first. $h(3)= 4\times3+5=17$.

Therefore, $g[h(3)]=g(17)= 2\times17+3=37$.

Answered: Can anyone please solve Q7 pg 329 in Induction?

Thu, 05 Dec 2019 22:10:12 +0000

Never mind, I was able to solve it myself. The trick was rearranging and factoring $5^{k+1}$ and add exponents to get $5^{k+2}$.

Individual and group relative strength in a fixed pool of players: How to approach the problem?

Tue, 29 Oct 2019 20:00:28 +0000

I apologize in advance if my question sounds too basic to be worthy of anyone's time, but statistics are not part of my curriculum.

I am developing a proof of concept of a web application modeling the contribution of individual soccer player with respect to the different teams they've played with throughout their career. In particular, I am looking into a way of ranking both individuals and groups of players as follows::

teammates relative strength: the best/worst combinations of players when playing in the same team in the same matches;
opponents relative strength: the best/worst combinations of players when playing in opposite teams in the same matches, i.e. which tuples of teammates are the best/worst against which;

I must admit I don't quite know how to approach the problem (as I said I have no formal education in statistics or data science). I would be very grateful if anyone could give me some directions. How should I frame this particular problem and what resources in statistics or machine learning (if indeed this is a task fit for machine learning, perhaps I am mistaken on this) would be appropriate to tackle it?

I am eager to learn, so both practical examples or theoretical references (book chapters, online articles, etc) would be very welcome.

Thanks in advance!

Answered: ideas and opinion on what kind of analyses needs to be done

Thu, 01 Aug 2019 12:31:05 +0000

It is a broad question and could have many answers. You can ask many questions and try to answer them based on your main business problem: "the impact of all the products that were discontinued last year on the customers and sales". For example:

You can analyze a hypothesis that discontinuing a specific product was a good decision or a bad decision?
- You can check the sale increase in similar not-discontinued products of the store to investigate if customers who used to buy the discontinued one still looking for it or not. You can build the null hypothesis and alternative hypothesis to analyze the impact statistically.
- You can review customer calls/emails after discontinuing and analyzing the sentiment of the customer for this decision. It could be done using machine learning.

You have to generate questions and try to answer them using the data you have. It totally depends on how you define impact in your statement. After generating the questions, and finding answers, you can recommend some advice based on your results. For example, if you find out discontinuing the product was a bad decision, you can recommend to continue it again.

Answered: What are the most important Python libraries for data science?

Mon, 08 Jul 2019 04:43:54 +0000

Based on what is offered here, The most important libraries are:

Python Libraries for Data Collection
- Beautiful Soup
- Scrapy
- Selenium
Python Libraries for Data Cleaning and Manipulation
- Pandas
- PyOD
- NumPy
- Spacy
Python Libraries for Data Visualization
- Matplotlib
- Seaborn
- Bokeh
Python Libraries for Modeling
- Scikit-learn
- TensorFlow
- PyTorch
Python Libraries for Model Interpretability
- Lime
- H2O
Python Libraries for Audio Processing
- Librosa
- Madmom
- pyAudioAnalysis
Python Libraries for Image Processing
- OpenCV-Python
- Scikit-image
- Pillow
Python Libraries for Database
- Psycopg
- SQLAlchemy
Python Libraries for Deployment
- Flask

Using aggregate data to generate observation-level data statistically sound?

Tue, 11 Jun 2019 22:04:01 +0000

Context: In the realm of Paid Search Marketing. Current reporting does not provide event level data only aggregate totals with different segments. Want to compare distributions/test statistical significance of A/B test results. Did not want to assume that data followed normal distribution or know STDEV for data so came with this approach.

My Question: I am going to use the average "CPA" or "CTR" for a date range, and generate an observation for each conversion based off the average for a time range. Is this statistically sound way if I want to generate raw data? Would I have wonky distributions because of the multiple averages? Just want a gutcheck if I'm completely off base.

My Aggregate data looks like below:

Day	Cost	Acquisition	CPA or CTR
1	40	2	$20
2	75	3	$25

Observation data I generate looks like below:

Day	Acquisition
1	$20
1	$20
2	$25
2	$25
2	$25

I really appreciate your help with this question! An important project to me at work.

Answered: What are the available libraries for continuous time hidden markov models ?

Tue, 11 Jun 2019 21:19:58 +0000

I found some implementations on Python by searching in GitHub. https://github.com/lopatovsky/HMMs is one of them.

Answer selected: How to use LinkedIn Learning (formerly Lynda.com) for free?

Wed, 22 May 2019 06:59:13 +0000

If you have a university or college email address, the following photos show you how to sign in Lynda.com using that account.

Click on Sign in on the top menu to see the following page:
Click on Sign in with Lynda, and then write your email address of your school or college (for example *Emails are not allowed*)
It will redirect you to the portal of your organization and after that, you can create a free account on Lynda to use all the contents for free as long as you can sign in this way.
Some city libraries' LinkedIn Learning:
- City of Toronto
- City of Richmond Hill

Answered: How to reshape in pandas dataframe?

Sat, 06 Apr 2019 21:12:13 +0000

Did you try to use reshape(-1,1)?

a = np.array([[1, 2, 3, 4],
         [5, 6, 7, 8],
         [9, 10, 11, 12]])
print(a.shape)
# output: (3, 4)

print(a.reshape(-1,1))
'''
output:
array([[ 1],
   [ 2],
   [ 3],
   [ 4],
   [ 5],
   [ 6],
   [ 7],
   [ 8],
   [ 9],
   [10],
   [11],
   [12]])
'''

Answered: Is digital marketing and marketing internships worth it for a data science student?

Fri, 05 Apr 2019 00:38:05 +0000

Depending on what you want to do, and what kind of methodologies you use for analyzing and reporting, an internship in this area could be considered as data science experience.

Answered: How to open Jupyter notebook files on Windows or Mac without web browser?

Sat, 16 Mar 2019 23:38:07 +0000

If you want an executable application that opens Jupyter notebooks for viewing, printing and executing, nterac (https://nteract.io/desktop) is here to serve you. On the other hand, it means we can open Jupyter notebooks as standalone executables, instead of running inside the browser.

However, If you just need preview and do not need to execute, and you have a Mac, use this: https://github.com/tuxu/nbviewer-app

https://cloud.githubusercontent.com/assets/836375/18421299/d95ad398-783b-11e6-8b23-d54cf7caad1e.png

Answer selected: Could you please share a good cheat sheet for logic gates?

Fri, 15 Mar 2019 17:38:23 +0000

Please take a look at the following table that contains most common logic gates and their truth tables. You can download it by clicking on it.

Passing variable length sentences to Tensorflow LSTM

Mon, 11 Feb 2019 05:06:27 +0000

I have a tensorflow LSTM model for predicting the sentiment. I build the model with the maximum sequence length 150. (Maximum number of words) While making predictions, i have written the code as below:

batchSize = 32
maxSeqLength = 150

def getSentenceMatrix(sentence):
    arr = np.zeros([batchSize, maxSeqLength])
    sentenceMatrix = np.zeros([batchSize,maxSeqLength], dtype='int32')
    cleanedSentence = cleanSentences(sentence)
    cleanedSentence = ' '.join(cleanedSentence.split()[:150])
    split = cleanedSentence.split()
    for indexCounter,word in enumerate(split):
        try:
            sentenceMatrix[0,indexCounter] = wordsList.index(word)
        except ValueError:
            sentenceMatrix[0,indexCounter] = 399999 #Vector for unkown words
    return sentenceMatrix

input_text = "example data"
inputMatrix = getSentenceMatrix(input_text)

In the code i'm truncating my input text to 150 words and ignoring remaining data.Due to this my predictions are wrong.

cleanedSentence = ' '.join(cleanedSentence.split()[:150])

I know that if we have lesser length than sequence length we can pad with zero's. What we need to do if we have more length. Can you suggest me the best way to do this. Thanks in advance.

Answered: What is degree of Freedom while calculating confidence interval?

Fri, 28 Dec 2018 16:06:55 +0000

Degree of Freedom is the number of values that are free to vary in the computation of a statistic. For more information please take a look at this article.

Answered: What is the easiest way to distinguish whether to use a z value test or a t value test?

Fri, 28 Dec 2018 16:03:46 +0000

Already is answered here. Most of the time we do not have distribution parameters, therefore, t-test is more practical.

Answered: How to find the strength of a P-value against a null hypothesis?

Fri, 28 Dec 2018 16:01:36 +0000

P > 0.10 --------------- Weak or None

0.05 < P <= 0.10 --------------- Moderate

0.01 < P <= 0.05 ---------------- Strong

P <= 0.01 ---------------------- Very Strong