fix minor typos (pymc-labs#1215)

docs/source/notebooks/mmm/mmm_example.ipynb

@@ -14,7 +14,7 @@
     "Before jumping into the data, let's first define the business problem we are trying to solve. We are a marketing agency and we want to optimize the marketing budget of a client. We have access to the following data:\n",
     "\n",
     "- Sales data: weekly sales of the client.\n",
-    "- Media spend data: weekly spend on different media channels (e.g. TV, radio, online, etc.). In ths example we consider 2 media channels: $x_{1}$ and $x_{2}$.\n",
+    "- Media spend data: weekly spend on different media channels (e.g. TV, radio, online, etc.). In this example we consider 2 media channels: $x_{1}$ and $x_{2}$.\n",
     "- Domain knowledge: \n",
     "  - We know that there has a been an positive sales trend which we believe comes from a strong economic growth.\n",
     "  - We also know that there is a yearly seasonality effect.\n",
@@ -127,13 +127,13 @@
     "1. On the one hand side, there is a carry-over effect. Meaning, the effect of spend on sales is not instantaneous but accumulates over time.\n",
     "2. In addition, there is a saturation effect. Meaning, the effect of spend on sales is not linear but saturates at some point.\n",
     "\n",
-    "The equation implemented to describe the DAG presented above will be the one expressed in [Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017)](https://research.google/pubs/pub46001/), adding a causal assumption around the media effects and their exclusively possitive impact. Concretely, given a time series target variable $y_{t}$ (e.g. sales or conversions), media variables $x_{m, t}$ (e.g. impressions, clicks or costs) and a set of control covariates $z_{c, t}$ (e.g. holidays, special events) we consider a linear model of the form \n",
+    "The equation implemented to describe the DAG presented above will be the one expressed in [Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017)](https://research.google/pubs/pub46001/), adding a causal assumption around the media effects and their exclusively positive impact. Concretely, given a time series target variable $y_{t}$ (e.g. sales or conversions), media variables $x_{m, t}$ (e.g. impressions, clicks or costs) and a set of control covariates $z_{c, t}$ (e.g. holidays, special events) we consider a linear model of the form \n",
     "\n",
     "$$\n",
     "y_{t} = \\alpha + \\sum_{m=1}^{M}\\beta_{m}f(x_{m, t}) +  \\sum_{c=1}^{C}\\gamma_{c}z_{c, t} + \\varepsilon_{t},\n",
     "$$\n",
     "\n",
-    "where $\\alpha$ is the intercept, $f$ is a media transformation function and $\\varepsilon_{t}$ is the error therm which we assume is normally distributed. The function $f$ encodes the positive media contribution on the target variable. Typically we consider two types of transformation: adstock (carry-over) and saturation effects.\n",
+    "where $\\alpha$ is the intercept, $f$ is a media transformation function and $\\varepsilon_{t}$ is the error term which we assume is normally distributed. The function $f$ encodes the positive media contribution on the target variable. Typically we consider two types of transformation: adstock (carry-over) and saturation effects.\n",
     "\n",
     "In **PyMC-Marketing**, we offer an API for a Bayesian Media Mix Model (MMM) with various specifications. In the example, we’ll implement `Geometric Adstock` and `Logistic Saturation` as the chosen transformations for our previously discussed Structural Causal Equation."
    ]