<prompt> Likarolo tsa Linear Regression Model ke life?nan  <response>: Instruction in English: What are the components of a Linear Regression Model? . Response in English: Linear Regression is a supervised learning algorithm used for predicting a continuous outcome variable also called the dependent variable based on one or more predictor variables also called independent variables or features. The goal of linear regression is to find the line of best fit that can accurately predict the outcome variable. The components of a linear regression model include:1. The Predictor variables: These are the independent variables that are used to make predictions on the outcome variable. In simple linear regression, there is only one predictor variable, while in multiple linear regressions there are more than one predictor variables.2. The Outcome variable: This is the dependent variable that we are trying to predict using the predictor variables. The outcome variable is continuous, meaning it can take on any value within a range.3. The Regression Coefficients: These are numerical values associated with the predictor variables that represent the change in the outcome variable for a one-unit change in the predictor variable. In simple linear regression, there is only one regression coefficient, while in multiple linear regression there is one regression coefficient for each predictor variable.4. The Intercept: This is the point where the line of best fit intersects the y-axis. It represents the value of the outcome variable when all the predictor variables are equal to zero. It is also called the bias term.5. The Residuals: These are the differences between the observed values of the outcome variable and the predicted values. The aim of the linear regression model is to minimize the sum of the squared residuals.6. The Equation of the line: The equation of the line of best fit is given by: y b0 b1x1 b2x2 ... bnxn where y is the outcome variable, b0 is the intercept, b1, b2, ... , bn are the regression coefficients and x1, x2, ... xn are the predictor variables. . Response in Sesotho: Linear Regression ke algorithm ea ho ithuta e hlokometsoeng e sebelisetsoang ho bolela esale pele phetoho e tsoelang pele ea sephetho eo hape e bitsoang phetoho e itšetlehileng ka eona e itšetlehileng ka mefuta e le ngoe kapa tse ngata tse bolelang esale pele tseo hape li bitsoang mefuta e ikemetseng kapa likarolo. Sepheo sa linear regression ke ho fumana moeli o nepahetseng ka ho fetisisa o ka bolelang ka nepo phetoho ea sephetho. Dikarolo tsa mmotlolo wa mola wa ho kgutlela morao di kenyeletsa: 1. Phapang dikarolo ya Predictor: Tsena ke mefuta e ikemetseng e sebedisetswang ho lepa diphetho mabapi le phapang ya sephetho. Ka mokhoa o bonolo oa ho khutlela morao, ho na le phapang e le ngoe feela ea ho bolela esale pele, athe ka litsela tse ngata tsa mela ho na le mefuta e fetang e le ngoe ea ho bolela esale pele. 2. Phapang ea Sephetho: Ena ke phapang e itšetlehileng ka eona eo re lekang ho e bolela esale pele ka ho sebelisa likarolo tsa ho bolela esale pele. Phapang ea sephetho e tsoela pele, ho bolelang hore e ka nka boleng bofe kapa bofe ka hara mefuta e mengata. 3. The Regression Coefficients: Tsena ke litekanyetso tsa linomoro tse amanang le li feto-fetohang tse emelang phetoho ea sephetho sa phetoho bakeng sa phetoho ea yuniti e le ngoe ho feto-fetoha ha ponelopele. Ka mokhoa o bonolo oa ho khutlela morao, ho na le coefficient e le ngoe feela ea ho khutlela morao, ha ho khutlela morao ka mela e mengata ho na le coefficient e le ngoe ea ho khutlela morao bakeng sa phetoho e ngoe le e ngoe ea ho bolela esale pele. 4. The Intercept: Ena ke ntlha eo mola oa ho lekana hantle o teanang le axis ea y. E emela boleng ba phetoho ea sephetho ha mefuta eohle ea li-predictor e lekana le zero. E boetse e bitsoa lentsoe la leeme. 5. The Residuals: Tsena ke liphapano lipakeng tsa boleng bo hlokometsoeng ba phapang ea sephetho le boleng bo boletsoeng esale pele. Sepheo sa mohlala oa linear regression ke ho fokotsa kakaretso ea masala a squared. 6. The Equation of the line: Equation ea mola oa ho lekana ka ho fetisisa e fanoa ke: y b0 b1x1 b2x2 ... bnxn moo y e leng phetoho ea sephetho, b0 ke thipa, b1, b2, ... , bn ke li-coefficients tsa regression me x1, x2, ... xn ke mefuta-futa ea selelekela. 