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H1: How to Interpret Multiple Regression Results in SPSS

H1: (SPSS တွင် Multiple Regression Results ကို ဘယ်လို အဓိပ္ပာယ်ဖွင့်ဆိုမလဲ)

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Multiple Linear Regression is one of the most frequently used statistical methods in postgraduate research, especially for MBA, MPA, MMM, Medicine, Public Health, Social Science, Business, and Management studies. Many researchers can run regression analysis in SPSS, but they often face difficulty when interpreting the results academically.

Multiple Regression ဆိုသည်မှာ Independent Variables များစွာက Dependent Variable တစ်ခုအပေါ် ဘယ်လောက်သက်ရောက်မှုရှိသလဲဆိုတာကို စစ်ဆေးရန် အသုံးပြုသော statistical method ဖြစ်သည်။

This guide explains the most important parts of a multiple regression output in a clear, practical, and bilingual way. It focuses not only on what the numbers mean, but also on why those numbers matter in research interpretation.

H2: What Is Multiple Linear Regression?

H2: (Multiple Linear Regression ဆိုတာဘာလဲ)

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Multiple Linear Regression is used when a researcher wants to examine the effect of two or more independent variableson one dependent variable.

For example:

Independent Variables:

• Talent Acquisition

• Training and Development

• Performance Management

• Rewards and Recognition

Dependent Variable:

• Employee Engagement

In this case, regression analysis answers the question:

“Which factors significantly affect employee engagement, and which factor has the strongest influence?”
“Employee Engagement ကို ဘယ်အချက်တွေက သက်ရောက်မှုရှိသလဲ၊ ဘယ်အချက်က အများဆုံးသက်ရောက်သလဲ” ဆိုတာကို Multiple Regression က ဖြေရှင်းပေးနိုင်သည်။

H2: Two Main Ways to Interpret Multiple Regression Results

H2: (Multiple Regression Results ကို အဓိပ္ပာယ်ဖွင့်ဆိုနိုင်သော နည်းလမ်းနှစ်မျိုး)

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Multiple regression results can usually be interpreted in two major ways.

H3: Method 1: Regression Equation Interpretation

H3: (နည်းလမ်း ၁ — Regression Equation ဖြင့် အဓိပ္ပာယ်ဖွင့်ဆိုခြင်း)

This method uses the Unstandardized Coefficients (B) to formulate a prediction equation.

It answers:

“If one independent variable increases by one unit, how much will the dependent variable increase or decrease?”
“Independent Variable တစ်ခု ၁ ယူနစ် တိုးလာရင် Dependent Variable က ဘယ်လောက်ပြောင်းလဲမလဲ” ဆိုတာကို ဖော်ပြသည်။

H3: Method 2: Coefficient-Based Interpretation

H3: (နည်းလမ်း ၂ — Unstandardized B နှင့် Standardized Beta ဖြင့် အဓိပ္ပာယ်ဖွင့်ဆိုခြင်း)

This method focuses on the significance and strength of each independent variable.

It answers:

“Which independent variable is the strongest predictor?”
“Independent Variables တွေထဲမှာ ဘယ်အချက်က အပြင်းထန်ဆုံး သက်ရောက်သလဲ” ဆိုတာကို ဖော်ပြသည်။

H2: How to Write a Regression Equation Using Unstandardized Coefficients

H2: (Unstandardized Coefficients ကိုသုံးပြီး Regression Equation ဘယ်လိုရေးမလဲ)

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A regression equation is written by using the Unstandardized Coefficients (B) from the SPSS coefficients table.

The general formula is:

Y = a + b₁X₁ + b₂X₂ + b₃X₃ + b₄X₄ + e

Where:

• Y = Dependent Variable

• a = Constant

• b₁, b₂, b₃, b₄ = Unstandardized Coefficients

• X₁, X₂, X₃, X₄ = Independent Variables

• e = Error term

For example:

Employee Engagement (Y) = -0.896 + 0.399(TA) + 0.343(TD) + 0.313(PM) + 0.248(RR)

Where:

• TA = Talent Acquisition

• TD = Training and Development

• PM = Performance Management

• RR = Rewards and Recognition

Important Note:
When writing a regression equation, researchers must use Unstandardized Coefficients (B), not Standardized Beta (β).

Regression Equation ရေးသားရာတွင် Unstandardized B ကိုသာ အသုံးပြုရမည်။ Standardized Beta ကို Equation ထဲတွင် မထည့်ရပါ။

H2: Why Use Unstandardized B in Regression Equation?

H2: (Regression Equation တွင် Unstandardized B ကို ဘာကြောင့်သုံးရသလဲ)

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Unstandardized B shows the actual expected change in the dependent variable when an independent variable increases by one unit.

For example:

If Talent Acquisition has B = 0.399, it means:

When Talent Acquisition increases by one unit, Employee Engagement is expected to increase by 0.399 units, assuming all other variables remain constant.

Talent Acquisition တန်ဖိုး ၁ ယူနစ်တိုးလာပါက အခြား Variables များ မပြောင်းလဲဘဲ ရှိနေသည်ဟုယူဆလျှင် Employee Engagement သည် 0.399 ယူနစ် တိုးလာမည်ဟု ခန့်မှန်းနိုင်သည်။

This is called ceteris paribus, meaning all other factors are held constant.

H2: What Does a Negative Constant Mean in Multiple Regression?

H2: (Regression တွင် Constant အနှုတ်ပြခြင်း၏ အဓိပ္ပာယ်)

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A negative constant is one of the most common questions in regression interpretation.

The constant, also called the Y-intercept, represents the expected value of the dependent variable when all independent variables are equal to zero.

For example:

Employee Engagement (Y) = -0.896 + 0.399(TA) + 0.343(TD) + 0.313(PM) + 0.248(RR)

Here, the constant is -0.896.

This means that if all independent variables such as Talent Acquisition, Training and Development, Performance Management, and Rewards and Recognition are zero, the predicted value of Employee Engagement would be -0.896.

However, this does not always have a practical real-life meaning, especially when the study uses a five-point Likert scale.

Likert Scale Research များတွင် Independent Variables အားလုံး သုညဖြစ်သည်ဆိုသော အခြေအနေသည် လက်တွေ့တွင် မရှိနိုင်သော အခြေအနေဖြစ်နိုင်သည်။ ထို့ကြောင့် Constant အနှုတ်ပြခြင်းကို လက်တွေ့အဓိပ္ပာယ်ထက် statistical calculation result အနေဖြင့် နားလည်သင့်သည်။

H3: Why Can the Constant Become Negative?

H3: (Constant က ဘာကြောင့် အနှုတ်ဖြစ်နိုင်သလဲ)

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A negative constant may occur because the regression line is mathematically extended to the Y-axis. When the model estimates the value of Y at X = 0, the predicted value may fall below zero.

This does not automatically mean that the model is wrong.

Researchers should explain it carefully:

• The constant is a mathematical intercept.

• It may not represent a realistic situation in Likert-scale research.

• The main interpretation should focus on the independent variables, their coefficients, significance values, and model fit.

ထို့ကြောင့် Constant အနှုတ်ဖြစ်ခြင်းသည် Regression Model မှားနေသည်ဟု မဆိုလိုပါ။ သို့သော် Viva, presentation, or research defence တွင် မေးခံရနိုင်သောကြောင့် အကျိုးသင့်အကြောင်းသင့်ရှင်းပြနိုင်ရန် လိုအပ်သည်။

H2: Unstandardized B vs Standardized Beta

H2: (Unstandardized B နှင့် Standardized Beta ကွာခြားချက်)

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Many researchers confuse Unstandardized B and Standardized Beta. They are related, but they are used for different purposes.

H3: When to Use Unstandardized B

H3: (Unstandardized B ကို ဘယ်အချိန်မှာ သုံးရမလဲ)

Use Unstandardized B when:

• Writing a regression equation

• Explaining actual unit change

• Predicting the dependent variable

• Interpreting how much Y changes when X increases by one unit

Unstandardized B သည် မူလ measurement scale အပေါ် အခြေခံသော coefficient ဖြစ်သည်။ ထို့ကြောင့် Regression Equation ရေးရန် အသုံးပြုရမည်။

H3: When to Use Standardized Beta

H3: (Standardized Beta ကို ဘယ်အချိန်မှာ သုံးရမလဲ)

Use Standardized Beta (β) when:

• Comparing the relative strength of independent variables

• Ranking the strongest and weakest predictors

• Explaining which variable has the greatest influence

• Comparing variables measured in different units

Standardized Beta သည် Variables များကို standardized လုပ်ပြီး နှိုင်းယှဉ်နိုင်စေရန် ဖော်ပြသော coefficient ဖြစ်သည်။ ထို့ကြောင့် “ဘယ် variable က အရေးကြီးဆုံးလဲ” ဆိုတာကို ဖော်ပြရာတွင် အသုံးပြုရသည်။

H2: How to Identify the Strongest Predictor in Multiple Regression

H2: (Multiple Regression တွင် Strongest Predictor ကို ဘယ်လိုသတ်မှတ်မလဲ)

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To identify the strongest predictor, researchers should compare the Standardized Beta (β) values, not the Unstandardized B values.

For example:

• Talent Acquisition: β = 0.388

• Training and Development: β = 0.342

• Performance Management: β = 0.312

• Rewards and Recognition: β = 0.246

In this case, Talent Acquisition is the strongest predictor because it has the highest Standardized Beta value.

Standardized Beta တန်ဖိုးအမြင့်ဆုံးရှိသော variable သည် Dependent Variable အပေါ် သက်ရောက်မှုအကြီးဆုံးရှိသော predictor ဖြစ်သည်။

A suitable academic interpretation is:

The comparison of Standardized Beta values indicates that Talent Acquisition is the strongest predictor of Employee Engagement, followed by Training and Development, Performance Management, and Rewards and Recognition. This suggests that recruitment and talent attraction practices play the most influential role in strengthening employee engagement.

H2: t-value vs P-value in Regression Analysis

H2: (Regression Analysis တွင် t-value နှင့် P-value ကွာခြားချက်)

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The t-value and p-value are both used to determine whether an independent variable has a statistically significant effect on the dependent variable.

However, they are not the same.

H3: What Is t-value in Regression?

H3: (Regression တွင် t-value ဆိုတာဘာလဲ)

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The t-value shows whether the coefficient of an independent variable is strong enough compared with its standard error.

In simple terms:

t-value tells us whether the effect is statistically strong.
t-value သည် Independent Variable တစ်ခု၏ သက်ရောက်မှု ခိုင်မာမှုရှိမရှိကို ဖော်ပြသည်။

A common rule is:

• If t > +1.96, the effect may be statistically significant.

• If t < -1.96, the effect may also be statistically significant.

• If the t-value is close to zero, the effect is usually weak.

For example, if the t-values range from 5.388 to 8.133, they are much higher than 1.96. This indicates that the variables have strong statistical effects.

H3: What Is P-value in Regression?

H3: (Regression တွင် P-value ဆိုတာဘာလဲ)

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The p-value, also shown as Sig. in SPSS, tells us whether the result is statistically significant.

It answers this question:

“Is the observed effect likely to be real, or could it have happened by random chance?”
“တွေ့ရှိထားသော သက်ရောက်မှုသည် တကယ်ရှိနိုင်သလား၊ သို့မဟုတ် တိုက်ဆိုင်မှုကြောင့် ဖြစ်နိုင်သလား” ဆိုတာကို P-value က ဖော်ပြသည်။

Common interpretation:

• p < 0.05 = statistically significant at the 5% level

• p < 0.01 = statistically significant at the 1% level

• p < 0.001 = highly statistically significant

In SPSS, if Sig. is shown as 0.000, it does not mean the probability is absolutely zero. It means the value is extremely small, usually p < 0.001.

SPSS တွင် Sig. = 0.000 ဟုပြပါက အမှားဖြစ်နိုင်ခြေ လုံးဝမရှိဟု မဆိုလိုပါ။ အလွန်သေးငယ်သော p-value ဖြစ်ပြီး p < 0.001 ဟုရေးသားခြင်းက ပို၍သင့်လျော်သည်။

H2: How to Interpret Statistical Significance in Regression

H2: (Regression တွင် Statistical Significance ကို ဘယ်လိုရှင်းပြမလဲ)

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A statistically significant result means that the independent variable has a meaningful effect on the dependent variable based on the sample data.

For example:

The p-values for all independent variables are below 0.01, indicating that all predictors have statistically significant effects on Employee Engagement at the 1% significance level.

In Burmese explanation:

Independent Variables များ၏ p-value များသည် 0.01 ထက်နည်းနေပါက ၎င်း Variables များသည် Dependent Variable အပေါ် 1% significance level တွင် statistically significant effect ရှိသည်ဟု အဓိပ္ပာယ်ဖွင့်ဆိုနိုင်သည်။

H2: Collinearity Statistics: Tolerance and VIF

H2: (Collinearity Statistics: Tolerance နှင့် VIF)

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Collinearity statistics are used to check whether the independent variables are too highly correlated with each other.

This problem is called multicollinearity.

Multicollinearity ဆိုသည်မှာ Independent Variables အချင်းချင်း အလွန်အကျွံ ဆက်စပ်နေခြင်းဖြစ်သည်။

If multicollinearity exists, the regression results may become unstable or misleading.

H3: What Is Tolerance in Regression?

H3: (Regression တွင် Tolerance ဆိုတာဘာလဲ)

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Tolerance indicates how much of an independent variable is not explained by other independent variables.

A common rule is:

• Tolerance > 0.10 = acceptable

• Tolerance < 0.10 = possible multicollinearity problem

If the tolerance values are above 0.80, this is very good.

Tolerance တန်ဖိုးသည် 0.10 ထက်ကြီးရမည်။ 0.80 ကျော်ရှိပါက Independent Variables များအကြား data overlap နည်းပြီး model သည် ပိုမိုယုံကြည်စိတ်ချရနိုင်သည်။

H3: What Is VIF in Regression?

H3: (Regression တွင် VIF ဆိုတာဘာလဲ)

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VIF means Variance Inflation Factor. It shows whether the variance of a coefficient is inflated because of high correlation among independent variables.

Common rules:

• VIF < 10 = acceptable

• VIF < 5 = more conservative and preferred

• VIF close to 1 = very good

If VIF values range from 1.095 to 1.224, there is no serious multicollinearity problem.

A suitable academic interpretation is:

The Tolerance values are above 0.10 and the VIF values are below 5. Therefore, the results confirm that there is no multicollinearity issue among the independent variables. This indicates that the predictors are statistically distinct and suitable for regression analysis.

Burmese explanation:

Tolerance တန်ဖိုးများသည် 0.10 ထက်မြင့်ပြီး VIF တန်ဖိုးများသည် 5 ထက်နည်းနေသဖြင့် Independent Variables များအကြား Multicollinearity ပြဿနာမရှိကြောင်း ဖော်ပြနိုင်သည်။ ထို့ကြောင့် Regression Model သည် ယုံကြည်စိတ်ချရသော အခြေအနေရှိသည်။

H2: How to Interpret R, R Square, and Adjusted R Square

H2: (R, R Square နှင့် Adjusted R Square ကို ဘယ်လိုအဓိပ္ပာယ်ဖွင့်ဆိုမလဲ)

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Model Summary is another important part of SPSS regression output. It usually includes R, R Square, and Adjusted R Square.

H3: What Is R in Regression?

H3: (Regression တွင် R ဆိုတာဘာလဲ)

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R is the multiple correlation coefficient. It shows the overall relationship between all independent variables combined and the dependent variable.

For example:

If R = 0.851, this indicates a strong positive relationship between the predictors and the dependent variable.

R = 0.851 ဆိုပါက Independent Variables များစုစုပေါင်းနှင့် Dependent Variable အကြား 85.1% အဆင့်ရှိသော strong positive relationship ရှိသည်ဟု ဖော်ပြနိုင်သည်။

H3: What Is R Square in Regression?

H3: (Regression တွင် R Square ဆိုတာဘာလဲ)

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R Square is the coefficient of determination. It explains how much variation in the dependent variable is explained by the independent variables.

For example:

If R Square = 0.725, it means that the independent variables explain 72.5% of the variance in the dependent variable.

Academic interpretation:

The R Square value of 0.725 indicates that 72.5% of the variation in Employee Engagement can be explained by the independent variables included in the model. The remaining 27.5% may be explained by other factors not included in the study.

Burmese explanation:

R Square = 0.725 ဆိုသည်မှာ Dependent Variable ပြောင်းလဲမှု၏ 72.5% ကို ဤ model ထဲရှိ Independent Variables များက ရှင်းပြနိုင်သည်ဟု ဆိုလိုသည်။ ကျန် 27.5% သည် model ထဲမပါသော အခြားအချက်များကြောင့် ဖြစ်နိုင်သည်။

H3: What Is Adjusted R Square?

H3: (Adjusted R Square ဆိုတာဘာလဲ)

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Adjusted R Square adjusts the R Square value by considering the number of independent variables in the model.

It is useful because R Square can increase simply by adding more predictors, even if those predictors are not truly useful.

Adjusted R Square သည် Independent Variables အရေအတွက်ကြောင့် R Square တန်ဖိုး အလွန်အကျွံမြင့်တက်သွားခြင်းကို ထိန်းညှိပေးသော တန်ဖိုးဖြစ်သည်။

If Adjusted R Square = 0.718, it means that after adjustment, the model still explains 71.8% of the variance in the dependent variable. This shows that the model has strong explanatory power.

H2: How to Interpret F-value in Regression

H2: (Regression တွင် F-value ကို ဘယ်လိုအဓိပ္ပာယ်ဖွင့်ဆိုမလဲ)

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The F-value tests whether the overall regression model is statistically significant.

It answers the question:

“Does the whole model significantly predict the dependent variable?”
“Regression Model တစ်ခုလုံးက Dependent Variable ကို ခန့်မှန်းရာတွင် အသုံးဝင်သလား” ဆိုတာကို F-value က စစ်ဆေးသည်။

For example:

If F = 97.530 and Sig. = 0.000, the model is statistically significant.

A suitable academic interpretation is:

The F-value is statistically significant at p < 0.001, indicating that the overall regression model is valid and useful for predicting the dependent variable. This confirms that the independent variables collectively explain a significant proportion of variation in the dependent variable.

Burmese explanation:

F-value သည် p < 0.001 တွင် significant ဖြစ်နေပါက Regression Model တစ်ခုလုံးသည် Dependent Variable ကို ခန့်မှန်းရန် အသုံးဝင်ပြီး model fit ကောင်းမွန်သည်ဟု ဖော်ပြနိုင်သည်။

H2: How to Write Regression Interpretation Professionally

H2: (Regression Results ကို Academic Style ဖြင့် ဘယ်လိုရေးမလဲ)

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A professional regression interpretation should include four major parts.

H3: 1. Explain Model Fit

H3: (Model Fit ကို ရှင်းပြခြင်း)

Mention:

• R

• R Square

• Adjusted R Square

• F-value

• Significance level

Example:

The model summary indicates that the independent variables have a strong relationship with the dependent variable. The R Square value shows that the model explains a substantial proportion of the variance. The significant F-value further confirms that the overall regression model is statistically valid.

H3: 2. Explain Individual Effects

H3: (Variable တစ်ခုချင်းစီ၏ သက်ရောက်မှုကို ရှင်းပြခြင်း)

Mention:

• Unstandardized B

• Standardized Beta

• t-value

• p-value

Example:

The coefficient results show that all independent variables have positive and statistically significant effects on the dependent variable. This means that improvement in each predictor is associated with improvement in the outcome variable.

H3: 3. Rank the Strongest Predictors

H3: (သက်ရောက်မှုအများဆုံး Variables များကို အစဉ်လိုက်ဖော်ပြခြင်း)

Use Standardized Beta for ranking.

Example:

Based on the Standardized Beta values, Talent Acquisition is the strongest predictor, followed by Training and Development, Performance Management, and Rewards and Recognition.

H3: 4. Check Multicollinearity

H3: (Multicollinearity ပြဿနာ ရှိ/မရှိ စစ်ဆေးခြင်း)

Mention:

• Tolerance

• VIF

Example:

The Tolerance and VIF values confirm that there is no multicollinearity problem among the predictors. Therefore, the regression results can be considered statistically reliable.

H2: Common Mistakes in Multiple Regression Interpretation

H2: (Multiple Regression Interpretation တွင် မကြာခဏမှားတတ်သော အချက်များ)

Body:
Researchers should avoid the following mistakes:

• Using Standardized Beta in the regression equation
  Regression equation must use Unstandardized B only.

• Saying Sig. = 0.000 means zero error
  It should be written as p < 0.001.

• Comparing strongest predictors using Unstandardized B
  Predictor strength should be compared using Standardized Beta.

• Ignoring VIF and Tolerance
  Multicollinearity must be checked before trusting regression coefficients.

• Over-interpreting the negative constant
  A negative constant may be a mathematical result, especially in Likert-scale research.

• Reporting numbers without explaining meaning
  Academic interpretation should explain the practical meaning behind the statistical values.

H2: Simple Regression Interpretation Template

H2: (အသုံးပြုနိုင်သော Regression Interpretation Template)

Body:
Researchers may adapt the following template:

The regression results indicate that the overall model is statistically significant. The R Square value shows that the independent variables explain a substantial proportion of the variance in the dependent variable. The F-value is significant, confirming that the model is suitable for prediction.

The coefficient results further show that the independent variables have positive and statistically significant effects on the dependent variable. Based on the Standardized Beta values, the strongest predictor is [Variable Name], followed by [Variable Name], [Variable Name], and [Variable Name].

The Tolerance values are above 0.10 and the VIF values are below 5, indicating that there is no multicollinearity problem among the independent variables. Therefore, the regression model can be considered statistically reliable and appropriate for interpretation.

H2: Final Advice for Researchers in Myanmar

H2: (Myanmar Researchers များအတွက် အကြံပြုချက်)

Body:
For postgraduate researchers, regression interpretation should not be treated as a process of memorising statistical terms. The most important skill is understanding what each value represents and how it supports the research objective.

Researchers should focus on these key questions:

1. Is the overall model significant?
   Model တစ်ခုလုံး significant ဖြစ်သလား။

2. How much variance does the model explain?
   Dependent Variable ပြောင်းလဲမှုကို Model က ဘယ်နှရာခိုင်နှုန်းရှင်းပြနိုင်သလဲ။

3. Which variables are statistically significant?
   ဘယ် Independent Variables တွေက significant ဖြစ်သလဲ။

4. Which variable is the strongest predictor?
   ဘယ် variable က သက်ရောက်မှုအများဆုံးလဲ။

5. Is there any multicollinearity problem?
   Independent Variables အချင်းချင်း အလွန်အကျွံဆက်စပ်နေခြင်း ရှိသလား။

A good regression interpretation is not merely a description of numbers. It is an academic explanation of how independent variables influence the dependent variable, how reliable the model is, and what the findings mean for the research problem.

ကောင်းမွန်သော Regression Interpretation ဆိုသည်မှာ ကိန်းဂဏန်းများကို ဖော်ပြခြင်းသာမက၊ ထိုကိန်းဂဏန်းများ၏ အဓိပ္ပာယ်၊ သက်ရောက်မှု၊ ယုံကြည်စိတ်ချရမှုနှင့် သုတေသနပြဿနာအပေါ် ရှင်းလင်းနိုင်မှုကို academic style ဖြင့် ဖော်ပြနိုင်ခြင်းဖြစ်သည်။