Based on the above binary logit model, the probability of bu…

Based on the above binary logit model, the probability of buy (i.e., P(buy=1) in the discounted group (i.e., discount=1) is (1)___________(a. higher, b. smaller; 2 points) than that of the non-discounted group (i.e., discount =0) at statistically (2) _______________ (a. insignificant, b. significant 10%, c. significant 5%, d. significant 1%; 2 points). In terms of marketing mix (price, product, promotion, and place), the result indicates the effect of the promotion on consumer demand is positive. 

You have a dataset of face images at 128×128 resolution, som…

You have a dataset of face images at 128×128 resolution, some are severely noisy (grainy camera shots). You want to classify each image into one of five expressions: happy, sad, angry, surprised, neutral. You decide to build: Autoencoder (AE) for denoising. CNN that classifies the AE’s output. GAN for data augmentation—generating extra images in each expression category. After some early success, you suspect domain mismatch and overfitting. Let’s see what goes wrong. — Angry is the smallest class in the dataset. You generate GAN samples to augment. A post-hoc analysis shows some generated “angry” faces look more “cartoonish” or “mildly annoyed” than truly “angry.” Which statements about possible solutions are valid?