Differences

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--- multiple_regression_exercise [2025/10/30 13:25] – [추가설명] hkimscil
+++ multiple_regression_exercise [2025/10/30 13:28] (current) – [추가설명] hkimscil
@@ Line 342: / Line 342: @@
     * Sales hat = **6.76500 - (0.0951)*Advertising**  --> **ShelveLocMedium** 인경우
   * Sales hat = 6.76500 + (-0.05412 + 0.35597) * Advertising  --> ShelveLocGood 인경우
-    * Sales hat = 6.76500 - (0.30185)*Advertising  --> ShelveLocGood 인경우
+    * **Sales hat = 6.76500 - (0.30185)*Advertising**  --> **ShelveLocGood** 인경우
 라고 하겠습니다.
@@ Line 440: / Line 440: @@
 </code>
 ) Bad 인 경우는
-  * Sales hat = 6.76500 + -0.05412*Advertising 이 됩니다.
+  * **Sales hat = 6.76500 + -0.05412*Advertising** 이 됩니다.
 ) ShelveLoc Good일 경우, ShelveLOcGood = 1이고 나머지는 0이므로
@@ Line 450: / Line 450: @@
 ) ShelveLoc Medium일 경우는
-Sales hat = 6.76500 + 0.09510 * Advertising 이 됩니다
+  * **Sales hat = 6.76500 + 0.09510 * Advertising** 이 됩니다
 위는 결국 lm.1 과 같은 모델이라는 뜻입니다.
-<code>
-> summary(lm.1)
-Call:
-lm(formula = Sales ~ Advertising + Advertising:ShelveLoc, data = Carseats)
-Residuals:
-    Min      1Q  Median      3Q     Max
--6.7650 -1.7351 -0.1523  1.5481  8.1350
-Coefficients:
-                            Estimate Std. Error t value Pr(>|t|)
-(Intercept)                  6.76500    0.17503  38.650  < 2e-16 ***
-Advertising                 -0.05412    0.03136  -1.726   0.0852 .
-Advertising:ShelveLocGood    0.35597    0.03901   9.126  < 2e-16 ***
-Advertising:ShelveLocMedium  0.14922    0.03346   4.460 1.07e-05 ***
----
-Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
-Residual standard error: 2.476 on 396 degrees of freedom
-Multiple R-squared:  0.237,	Adjusted R-squared:  0.2312
-F-statistic:    41 on 3 and 396 DF,  p-value: < 2.2e-16
->
-</code>