**IGNOU BECC 110 Solved Assignment 2022-23 , BECC 110 INTRODUCTORY ECONOMETRICS ****Solved Assignment 2022-23 Download Free :** **BECC 110 Solved Assignment 2022-2023** , IGNOU BECC 110 Assignment 2022-23, BECC 110 Assignment 2022-23 , BECC 110 Assignment , BECC 110 INTRODUCTORY ECONOMETRICS Solved Assignment 2022-23 Download Free IGNOU Assignments 2022-23- **BACHELOR OF ARTS Assignment 2022-23** Gandhi National Open University had recently uploaded the assignments of the present session for BACHELOR OF ARTS Programme for the year 2022-23. Students are recommended to download their Assignments from this webpage itself. Study of Political Science is very important for every person because it is interrelated with the society and the molar values in today culture and society. IGNOU solved assignment 2022-23 ignou dece solved assignment 2022-23, ignou ma sociology assignment 2022-23 meg 10 solved assignment 2022-23 ts 6 solved assignment 2022-23 , meg solved assignment 2022-23 .

## IGNOU BECC 110 Solved Assignment 2022-23

**We provide handwritten PDF and Hardcopy to our IGNOU and other university students. There are several types of handwritten assignment we provide all Over India. BECC 110 INTRODUCTORY ECONOMETRICS ****Solved Assignment 2022-23 Download Free We have worked in this field for so long. You can get your assignment done – 8130208920**

**Important Note – IGNOU BECC 110 ****Solved Assignment 2022-2023 Download Free You may be aware that you need to submit your assignments before you can appear for the Term End Exams. Please remember to keep a copy of your completed assignment, just in case the one you submitted is lost in transit.**

**Submission Date : **

**31st March 2033 (if enrolled in the July 2033 Session)****30th Sept, 2033 (if enrolled in the January 2033 session).**

Answer the following Descriptive Category questions in about 500 words each. Each question carries 20 marks. Word limit does not apply in case of numerical questions in Assignment One.

Answer the following Middle Category questions in about 250 words each. Each question carries 10 marks. Word limit does not apply in case of numerical questions in Assignment Two.

Answer the following Short Category questions in about 100 words each. Each question carries 6 marks in Assignment Three.

Answer all the questions.

**Assignment One**

**1. (a) Distinguish between the Population Regression Function and Sample Regression Function in detail. Use appropriate diagram to substantiate your response.**

Let’s investigate this question with another example. Below is a plot illustrating a potential relationship between the predictor “high school grade point average (gpa)” and the response “college entrance test score.” Only four groups (“subpopulations”) of students are considered — those with a gpa of 1, those with a gpa of 2, …, and those with a gpa of 4.

Let’s focus for now just on those students who have a gpa of 1. As you can see, there are so many data points — each representing one student — that the data points run together. That is, the data on the entire subpopulation of students with a gpa of 1 are plotted. And, similarly, the data on the entire subpopulation of students with gpas of 2, 3, and 4 are plotted.

Now, take the average college entrance test score for students with a gpa of 1. And, similarly, take the average college entrance test score for students with a gpa of 2, 3, and 4. Connecting the dots — that is, the averages — you get a line, which we summarize by the formula μY=E(Y)=β0+β1x. The line — which is called the “**population regression line**” — summarizes the trend *in the population* between the predictor *x* and the mean of the responses* μ*_{Y}. We can also express the average college entrance test score for the *i*-th student, E(Yi)=β0+β1xi. Of course, not every student’s college entrance test score will equal the average E(Yi). There will be some error. That is, any student’s response *y** _{i}* will be the linear trend β0+β1xi plus some error ϵi. So, another way to write the simple linear regression model is yi=E(Yi)+ϵi=β0+β1xi+ϵi.

When looking to summarize the relationship between a predictor *x* and a response *y*, we are interested in knowing the population regression line μY=E(Y)=β0+β1x. The only way we could ever know it, though, is to be able to collect data on everybody in the population — most often an impossible task. We have to rely on taking and using a sample of data from the population to estimate the population regression line.

Let’s take a sample of three students from each of the subpopulations — that is, three students with a gpa of 1, three students with a gpa of 2, …, and three students with a gpa of 4 — for a total of 12 students. As the plot below suggests, the least squares regression line y^=b0+b1x through the sample of 12 data points estimates the population regression line μY=E(Y)=β0+β1x. That is, the sample intercept *b*_{0} estimates the population intercept *β*_{0} and the sample slope *b*_{1} estimates the population slope *β*_{1}.

The least squares regression line doesn’t match the population regression line perfectly, but it is a pretty good estimate. And, of course, we’d get a different least squares regression line if we took another (different) sample of 12 such students. Ultimately, we are going to want to use the sample slope *b*_{1} to learn about the parameter we care about, the population slope *β*_{1}. And, we will use the sample intercept *b*_{0} to learn about the population intercept *β*_{0}.

In order to draw any conclusions about the population parameters *β*_{0} and *β*_{1}, we have to make a few more assumptions about the behavior of the data in a regression setting. We can get a pretty good feel for the assumptions by looking at our plot of gpa against college entrance test scores.

First, notice that when we connected the averages of the college entrance test scores for each of the subpopulations, it formed a line. Most often, we will not have the population of data at our disposal as we pretend to do here. If we didn’t, do you think it would be reasonable to assume that the mean college entrance test scores are **linearly related** to high school grade point averages?

Again, let’s focus on just one subpopulation, those students who have a gpa of 1, say. Notice that most of the college entrance scores for these students are clustered near the mean of 6, but a few students did much better than the subpopulation’s average scoring around a 9, and a few students did a bit worse scoring about a 3. Do you get the picture? Thinking instead about the errors, ϵi, most of the errors for these students are clustered near the mean of 0, but a few are as high as 3 and a few are as low as -3. If you could draw a probability curve for the errors above this subpopulation of data, what kind of a curve do you think it would be? Does it seem reasonable to assume that the errors for each subpopulation are **normally distributed**?

Looking at the plot again, notice that the spread of the college entrance test scores for students whose gpa is 1 is similar to the spread of the college entrance test scores for students whose gpa is 2, 3, and 4. Similarly, the spread of the errors is similar, no matter the gpa. Does it seem reasonable to assume that the errors for each subpopulation have **equal variance**?

Does it also seem reasonable to assume that the error for one student’s college entrance test score is **independent** of the error for another student’s college entrance test score? I’m sure you can come up with some scenarios — cheating students, for example — for which this assumption would not hold, but if you take a random sample from the population, it should be an assumption that is easily met.

We are now ready to summarize the four conditions or assumptions that underlie “**the simple linear regression model**:”

- The mean of the response, E(Yi), at each value of the predictor, xi, is a
**Linear function**of the xi. - The errors,
*ε*, are_{i}**Independent**. - The errors,
*ε*, at each value of the predictor, xi, are_{i}**Normally distributed**. - The errors,
*ε*, at each value of the predictor, xi, have_{i}**Equal variances**(denoted*σ*^{2}).

Do you notice what the first letters that are colored in blue spell? “**LINE**.” And, what are we studying in this course? Lines! Get it? You might find this mnemonic a useful way to remember the four conditions that make up what we call the “simple linear regression model.” Whenever you hear “simple linear regression model,” think of these four conditions!

An equivalent way to think of the first (linearity) condition is that the mean of the error, E(ϵi), at each value of the predictor, xi, is **zero**. An alternative way to describe all four assumptions is that the errors, ϵi, are independent normal random variables with mean zero and constant variance, σ2.

**(b) What are the assumptions of a classical regression model?**

Ordinary Least Squares (OLS) is the most common estimation method for linear models—and that’s true for a good reason. As long as your model satisfies the OLS assumptions for linear regression, you can rest easy knowing that you’re getting the best possible estimates.

Regression is a powerful analysis that can analyze multiple variables simultaneously to answer complex research questions. However, if you don’t satisfy the OLS assumptions, you might not be able to trust the results.

In this post, I cover the OLS linear regression assumptions, why they’re essential, and help you determine whether your model satisfies the assumptions.

Regression analysis is like other inferential methodologies. Our goal is to draw a random sample from a population and use it to estimate the properties of that population.

In regression analysis, the coefficients in the regression equation are estimates of the actual population parameters. We want these coefficient estimates to be the best possible estimates!

Suppose you request an estimate—say for the cost of a service that you are considering. How would you define a reasonable estimate?

- The estimates should tend to be right on target. They should not be systematically too high or too low. In other words, they should be unbiased or correct on average.
- Recognizing that estimates are almost never exactly correct, you want to minimize the discrepancy between the estimated value and actual value. Large differences are bad!

These two properties are exactly what we need for our coefficient estimates!

When your linear regression model satisfies the OLS assumptions, the procedure generates unbiased coefficient estimates that tend to be relatively close to the true population values (minimum variance). In fact, the Gauss-Markov theorem states that OLS produces estimates that are better than estimates from all other linear model estimation methods when the assumptions hold true.

**The Seven Classical OLS Assumptions**

Like many statistical analyses, ordinary least squares (OLS) regression has underlying assumptions. When these classical assumptions for linear regression are true, ordinary least squares produces the best estimates. However, if some of these assumptions are not true, you might need to employ remedial measures or use other estimation methods to improve the results.

Many of these assumptions describe properties of the error term. Unfortunately, the error term is a population value that we’ll never know. Instead, we’ll use the next best thing that is available—the residuals. Residuals are the sample estimate of the error for each observation.

Residuals = Observed value – the fitted value

When it comes to checking OLS assumptions, assessing the residuals is crucial!

There are seven classical OLS assumptions for linear regression. The first six are mandatory to produce the best estimates. While the quality of the estimates does not depend on the seventh assumption, analysts often evaluate it for other important reasons that I’ll cover.

This assumption addresses the functional form of the model. In statistics, a regression model is linear when all terms in the model are either the constant or a parameter multiplied by an independent variable. You build the model equation only by adding the terms together. These rules constrain the model to one type:

In the equation, the betas (βs) are the parameters that OLS estimates. Epsilon (ε) is the random error.

In fact, the defining characteristic of linear regression is this functional form of the *parameters* rather than the ability to model curvature. Linear models can model curvature by including nonlinear *variables* such as polynomials and transforming exponential functions.

**2). (a) Measurement error in variables is a serious problem in econometric studies. Find out the consequences of measurement errors in i) dependent variable and ii) independent variables.**

## Assignment Two

## 3. Differentiate between Chi-square distribution and t-distribution.

4. What is an estimator? Explain all the properties of an estimator with reference to BLUE.

5. Two variable regression model could have three functional forms as given below:

## 𝑌i = 𝛽1 + 𝛽2𝑋i + 𝑢i

𝑙𝑛𝑌i = 𝛽1 + 𝛽2𝑋i + 𝑢i

ln𝑌i = 𝛽1 + 𝛽2lnXi + 𝑢i

## How will you decide which is the best model for a given econometric problem?

**IGNOU Handwritten Hardcopy , WhatsApp – 8130208920**

**IGNOU BECC 110 Solved Assignment 2022-2023 We provide handwritten PDF and Hardcopy to our IGNOU and other university students. There are several types of handwritten assignment we provide all Over India. BECC 110 INTRODUCTORY ECONOMETRICS ****Solved ****Assignment 2022-23 Download Free We are genuinely work in this field for so many time. You can get your assignment done – 8130208920**

**GET IGNOU Handwritten Hardcopy , WhatsApp – 8130208920**

**GET IGNOU Handwritten Hardcopy , WhatsApp – 8130208920**

## Assignment Three

## 6. Discuss the remedial measures of multicollinearity.

7. What do we mean by Normal Distribution? Explain with the help of a diagram.

8. There are two types of estimation of parameters: Point Estimation and Interval Estimation. Explain the interval estimation method briefly.

9. What are the three methods of estimation? Discuss.

10. Explain the rejection regions for small samples and large samples.

**Get IGNOU BECC 110 Solved Assignment 2022-23 Download Free Now here from this website.**

**IGNOU BECC 110 Solved Assignment 2022-2023 get here all ignou solved assignment 2022-23 , ignou guess paper , ignou help books and ignou exam related material. We help students to get their assignment done with our handwritten services, BECC 110 INTRODUCTORY ECONOMETRICS Solved ****Assignment 2022-23 Download Free you can access our all material and services through WhatsApp also , 8130208920**

**GET SOLVED PDF – Click Here**

**IGNOU Instructions for the BECC 110**** INTRODUCTORY ECONOMETRICS ****Solved Assignment 2022-23**

IGNOU BECC 110 Solved Assignment 2022-2023 Download Free Before attempting the assignment, please read the following instructions carefully.

- Read the detailed instructions about the assignment given in the Handbook and Programme Guide.
- Write your
__enrolment number, name, full address and date on the top right corner of the first page of your response sheet__(s). - Write the course title, assignment number and the name of the study centre you are attached to in the centre of
__the first page of your response sheet__(s). for your response and tag all the pages carefully__Use only foolscap size paper__- Write the relevant question number with each answer.
- You should write in
__your own handwriting__.

**GUIDELINES FOR IGNOU Assignments 2022-23**

**IGNOU BECC 110 Solved Assignment 2022-23** You will find it useful to keep the following points in mind:

- Planning: Read the questions carefully. IGNOU BECC 110 Assignment 2022-23 Download Free Download PDF Go through the units on which they are based. Make some points regarding each question and then rearrange these in a logical order. And please write the answers in your own words. Do not reproduce passages from the units.
- Organisation: Be a little more selective and analytic before drawing up a rough outline of your answer. In an essay-type question, give adequate attention to your introduction and conclusion. IGNOU BECC 110 Solved Assignment 2022-2023 Download Free Download PDF The introduction must offer your brief interpretation of the question and how you propose to develop it. The conclusion must summarise your response to the question. In the course of your answer, you may like to make references to other texts or critics as this will add some depth to your analysis.
- Presentation: IGNOU BECC 110 Solved Assignment 2022-2023 Download Free Download PDF Once you are satisfied with your answers, you can write down the final version for submission, writing each answer neatly and underlining the points you wish to emphasize.

**IGNOU Assignment Front Page**

**The top of the first page of your response sheet should look like this: **Get IGNOU Assignment Front page through. And Attach on front page of your assignment. Students need to compulsory attach the front page in at the beginning of their handwritten assignment.

**ENROLMENT NO: …………………………………………………….**

**NAME: ……………………………………………………………………**

**ADDRESS: ……………………………………………………………… **

**COURSE TITLE: ……………………………………………………… **

**ASSIGNMENT NO: ………………………………………………… **

**STUDY CENTRE: …………………………………………….…….. **

**DATE: ……………………………………………………………………**

**BECC 110 Handwritten Assignment 2022-23**

**IGNOU BECC 110 ****Solved Assignment 2022-23 – We provide handwritten PDF and Hardcopy to our IGNOU and other university students. BECC 110 INTRODUCTORY ECONOMETRICS Solved Assignment 2022-23 Download Free Download PDF There are several types of handwritten assignment we provide all Over India. BECC 110 INTRODUCTORY ECONOMETRICS ****Solved Assignment 2022-23 Download Free Download PDF We are genuinely work in this field for so many time. You can get your assignment done –8130208920**

**Related Material Also –**

**IGNOU BPCC 104 Solved Assignment 2022-23****IGNOU BPYC 134 Solved Assignment 2022-23****IGNOU BPAC 104 Solved Assignment 2022-23****IGNOU BPCC 132 Solved Assignment 2022-23****IGNOU BANS 183 Solved Assignment 2022-23****IGNOU BHIE 142 Solved Assignment 2022-23****IGNOU BPSC 102 Solved Assignment 2022-23****IGNOU BPCC 101 Solved Assignment 2022-23**

**IGNOU BHIC 101 Solved Assignment 2022-23****IGNOU MAN 007 Solved Assignment 2022-23****IGNOU BPCC 134 Solved Assignment 2022-23**

**BUY PDF & Handwritten**

**Solved PDF Cost – @50 rs per Paper / Subject****Handwritten Hardcopy – @350 rs per paper/ subject**

**WhatsApp – 813020892**