update errata

errata.md

@@ -1,13 +1,22 @@
 | Page | Printed text | Correct text | Thanks |
 |------|--------------|--------------|------|
-|  88 (exercise 4) | wrong  short url  | The link should point to https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#case-study-2-coal-mining-disasters  | DrEntropy |
+| 62 | We will explore the value **of** over a grid of 200 points. |  We will explore the value over a grid of 200 points.| dweights |
+| 88 (exercise 4) | wrong  short url  | The link should point to https://www.pymc.io/projects/docs/en/stable/learn/core_notebooks/pymc_overview.html#case-study-2-coal-mining-disasters  | DrEntropy |
+| 94 | ...the variance between observed and theoretical values should be the same for all groups.| ...the variance between observed and theoretical values should be unique for each group. |  Kenji Oman  |
 | 104 | Figure 3.6 indicates a HalfNormal |  Figure 3.6 should indicate a Gamma | Jacob Warren  |
+| 115 | We are going to *usetemperature* | We are going to *use temperature* | Kenji Oman |
 | 115 | The noise term is 𝜖 | The noise term is 𝜎 | Parrenin Frédéric |
+| 116 | We **commit** it because otherwise... | We **omit** it because otherwise... | Kenji Oman |
 | 122  | The variance of the NegativeBinomial is 𝜇 + 𝜇²/𝛼 , so the larger the value of 𝛼 the **larger** the variance. | The variance of the NegativeBinomial is 𝜇 + 𝜇²/𝛼 , so the larger the value of 𝛼 the **smaller** the variance.     |  Tomás Capretto  |
+| 124 | (or data with a few bulk points)  | (or data with **only** a few bulk points) | Kenji Oman |
 | 133 | We have been using the linear motif to model the mean of a distribution **and, in the previous section, we used it to model interactions.** In statistics,... | We have been using the linear motif to model the mean of a distribution.  In statistics,...  | Jacob Warren |
 | 145  | In **the next chapter**, we will learn more about linear regression... | In **Chapter 6**, we will learn more about linear regression... | Tomás Capretto |
 |  191 |  The utility of **plot_cap** ... | The utility of **plot_predictions**... | Tomás Capretto  |
 | 194 | model_poly4 = bmb.Model("rented ∼ poly(**temperature**, degree=4)", bikes, | model_poly4 = bmb.Model("rented ∼ poly(**hour**, degree=4)", bikes, | Jacob Warren |
 | 195 | Figure 6.5: Posterior mean and posterior predictive distribution for the bikes model with temperature and humidity | Figure 6.5: Posterior mean and posterior predictive distribution for the polynomial bikes models with hour. | Jacob Warren |
 |  207 | We have been using **bmb.interpret_plot_predictions** ... One of them is **bmb.interpret_plot_comparisons**. | We have been using **bmb.interpret.plot_predictions** ... One of them is **bmb.interpret.plot_comparisons**.|  Tomás Capretto  |
-| 208  | Another useful function is **bmb.interpret_plot_slopes** | Another useful function is **bmb.interpret.plot_slopes**  |  Tomás Capretto |
+| 208  | Another useful function is **bmb.interpret_plot_slopes** | Another useful function is **bmb.interpret.plot_slopes**  |  Tomás Capretto |
+| 254 | We call **𝜙** the inverse link function and 𝜙 is... | We call **𝜓** the inverse link function and 𝜙 is... | Jacob Warren |
+
+
+Note: on page 23, Figure 1.9 shows the kurtosis. What PreliZ actually computes is the "excess kurtosis", i.e the kurtosis -3. Thanks to Narinder Singh for pointing this out.