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18.04.202514:55

14.04.202509:14

linear, binary, and multinomial regressions

🍎Linear Regression:
👉🏿Linear regression is a statistical modeling technique used to analyze the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the variables, where the dependent variable can be predicted as a linear function of the independent variables.
👉🏿The general formula for a simple linear regression model is Y = α + βX + ε, where Y is the dependent variable, X is the independent variable, α is the y-intercept, β is the slope or regression coefficient, and ε is the error term.
👉🏿Multiple linear regression expands on this by incorporating multiple independent variables, with the formula taking the form Y = α + β1X1 + β2X2 + ... + βnXn + ε.
🪐Linear regression is commonly used to model continuous, quantitative outcomes based on one or more predictor variables.

🍏Binary Logistic Regression:
🙏Binary logistic regression is a statistical modeling technique used when the dependent variable is dichotomous, meaning it has only two possible outcomes, such as "success" or "failure," "yes" or "no," or "presence" or "absence." The model estimates the probability of the dependent variable being in one of the two categories based on the values of the independent variables.
🙏The general formula for a binary logistic regression model is log(p/(1-p)) = α + βX, where p is the probability of the dependent variable being in one of the two categories, α is the y-intercept, β is the regression coefficient, and X is the independent variable(s).
🙏Binary logistic regression is commonly used in fields like medical diagnosis, marketing, and social sciences to predict the likelihood of a binary outcome.

🌍Multinomial Logistic Regression:
✍️Multinomial logistic regression is an extension of binary logistic regression, used when the dependent variable has more than two categorical outcomes. This technique allows for the modeling of the probability of each outcome category based on the values of the independent variables.
✍️The general formula for a multinomial logistic regression model is log(p_i/p_k) = α_i + β_iX, where p_i is the probability of the i-th outcome category, p_k is the probability of the reference or comparison category, α_i is the intercept for the i-th outcome category, β_i is the vector of regression coefficients for the i-th outcome category, and X is the vector of independent variables.
✍️Multinomial logistic regression is commonly used in social sciences, market research, and other fields where the dependent variable has more than two unordered categories.
If you need support related to:
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30.03.202504:30

ዉድ የቻናላችን የእስልምና እምነት ተከታይ ቤተሰቦች ወዳጅ ዘመዶች በጠቅላላ እንኳን ለኢድ አል-ፈጥር በዓል በሰላም አደረሳቹ!
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03.03.202507:43

Research Hypotheses and Research Questions
🍏Research hypotheses are predictive statements about the relationship between variables.
🍏Research questions are similar to hypotheses, except that they do not entail specific predictions and are phrased in question format. For example, one might have the following research question: "Is there a difference in students' scores on a standardized test if they took two tests in one day versus taking only one test on
each of two days?"
🍎A hypothesis regarding the same issue might be: "Students who take only one test
per day will score better on standardized tests than will students who take two tests in one day."
We divide research questions into three broad types: difference, associational, and descriptive.
👍Difference research questions: For these questions, we compare scores (on the dependent variable) of two or more different groups, each of which is composed of individuals with one of the values or levels on the independent variable. This type of question attempts to demonstrate that groups are not the same on the dependent variable.
👍Associational research questions: are those in which two or more variables are associated or related. This approach usually involves an attempt to see how two or more variables covary (as one grows larger, the other grows larger or smaller) or how one or more variables enables one to predict another variable.
👍Descriptive research questions: are not answered with inferential statistics. They merely describe or
summarize data, without trying to generalize to a larger population of individuals.

continue……………………………….
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Research related tasks
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# Data analysis (SPSS, STATA, ...) and others,

Training on basic statistical software's

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               🗝 STATA
                                 🗝 SPSS
                                               🗝 R
                                                        🗝 XLSTAT
🪜And other related software's...... also any other Questions please contact us via
☎️
+251912688642
+251912688642

Telegram Account
https://t.me/Research100stock
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